{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Ya9l3vTiUwPy" }, "source": [ "# Introduction to Pandas and other libraries\n", "\n", "(Many thanks to Evimaria Terzi and Mark Crovella for their code and examples)" ] }, { "cell_type": "markdown", "metadata": { "id": "yq6cmw_ZUwP1" }, "source": [ "## Pandas\n", "\n", "Pandas is the Python Data Analysis Library.\n", "\n", "Pandas is an extremely versatile tool for manipulating datasets, mostly tabular data. You can think of Pandas as the evolution of excel spreadsheets, with more capabilities for coding, and SQL queries such as joins and group-by. \n", "\n", "It also produces high quality plots with matplotlib, and integrates nicely with other libraries that expect NumPy arrays.\n", "\n", "You can find more details here" ] }, { "cell_type": "markdown", "metadata": { "id": "4MShsNxsUwP2" }, "source": [ "### Storing data tables\n", "\n", "Most data can be viewed as tables or matrices (in the case where all entries are numeric). The rows correspond to objects and the columns correspond to the attributes or features.\n", "\n", "There are different ways we can store such data tables in Python" ] }, { "cell_type": "markdown", "metadata": { "id": "TSKPlnqLUwP3" }, "source": [ "**Two-dimensional lists**" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "id": "QB5uLmm6UwP4", "outputId": "4d33f9b1-bee6-4a35-f0d8-3b11344e0480", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[[0.3, 10, 1000], [0.5, 2, 509], [0.4, 8, 789]]\n" ] } ], "source": [ "D = [[0.3, 10, 1000],[0.5,2,509],[0.4, 8, 789]]\n", "print(D)" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "id": "sTxp-CT4UwP5", "outputId": "6cd2676f-1434-4e1e-8aa5-3bdc7ba8d4a8", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[[30000, 'Married', 1], [20000, 'Single', 0], [45000, 'Maried', 0]]\n" ] } ], "source": [ "D = [[30000, 'Married', 1],[20000,'Single', 0],[45000, 'Maried', 0]]\n", "print(D)" ] }, { "cell_type": "markdown", "metadata": { "id": "kZX2bAHYUwP5" }, "source": [ "**Numpy Arrays**\n", "\n", "Numpy is a the library of Python for numerical computations and matrix manipulations. It has a lot of the functionality of Matlab but also allows for data analysis operations (similar to Pandas). Read more for Numpy here: http://www.numpy.org/\n", "\n", "The Array is the main data structure for numpy. It stores multidimensional **numeric** tables." ] }, { "cell_type": "markdown", "metadata": { "id": "bwTn1GSyUwP5" }, "source": [ "We can create numpy arrays from lists" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qSP4xk0SUwP6", "outputId": "25f9b995-724d-4b5f-cb0a-3e7271090a8a" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", " Deterministic 1-dimensional array \n", "\n", "[ 2 5 18 14 4]\n", "\n", " Deterministic 2-dimensional array \n", "\n", "[[ 2 5 18 14 4]\n", " [12 15 1 2 8]]\n" ] } ], "source": [ "import numpy as np\n", "\n", "#1-dimensional array\n", "x = np.array([2,5,18,14,4])\n", "print (\"\\n Deterministic 1-dimensional array \\n\")\n", "print (x)\n", "\n", "#2-dimensional array\n", "x = np.array([[2,5,18,14,4], [12,15,1,2,8]])\n", "print (\"\\n Deterministic 2-dimensional array \\n\")\n", "print (x)" ] }, { "cell_type": "markdown", "metadata": { "id": "sF_l1DIMUwP6" }, "source": [ "There are also numpy operations that create arrays of different types" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7nUCK7lIUwP6", "outputId": "4e282f77-2f6c-4aba-9ba4-41ee4fdd6035" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", " Random 5x5 2-dimensional array \n", "\n", "[[0.32040338 0.90094031 0.01712409 0.19855107 0.11575991]\n", " [0.39457475 0.43835629 0.15191193 0.73708753 0.57972269]\n", " [0.42109204 0.03484573 0.92496324 0.12796655 0.03550831]\n", " [0.41460175 0.77965859 0.25871421 0.27506664 0.85571966]\n", " [0.33514513 0.46550202 0.68617055 0.54611226 0.80606934]]\n", "\n", " 4x4 array with ones \n", "\n", "[[1. 1. 1. 1.]\n", " [1. 1. 1. 1.]\n", " [1. 1. 1. 1.]\n", " [1. 1. 1. 1.]]\n", "\n", " Diagonal matrix\n", "\n", "[[1 0 0]\n", " [0 2 0]\n", " [0 0 3]]\n" ] } ], "source": [ "x = np.random.rand(5,5)\n", "print (\"\\n Random 5x5 2-dimensional array \\n\")\n", "print (x)\n", "\n", "x = np.ones((4,4))\n", "print (\"\\n 4x4 array with ones \\n\")\n", "print (x)\n", "\n", "x = np.diag([1,2,3])\n", "print (\"\\n Diagonal matrix\\n\")\n", "print(x)" ] }, { "cell_type": "markdown", "metadata": { "id": "wopOqGuLUwP6" }, "source": [ "Why do we need numpy arrays? Because we can do different linear algebra operations on the numeric arrays\n", "\n", "For example:" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5n4yO5u5UwP7", "outputId": "fb824b23-b7e5-41da-8e93-ebfae41c26d6" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", " Random 2x3 array with integers\n", "[[6 7 8]\n", " [9 4 8]]\n", "\n", " Transpose of the matrix \n", "\n", "[[6 9]\n", " [7 4]\n", " [8 8]]\n", "\n", " Matrix 2x+1 \n", "\n", "[[13 15 17]\n", " [19 9 17]]\n" ] } ], "source": [ "x = np.random.randint(10,size=(2,3))\n", "print(\"\\n Random 2x3 array with integers\")\n", "print(x)\n", "\n", "#Matrix transpose\n", "print (\"\\n Transpose of the matrix \\n\")\n", "print (x.T)\n", "\n", "#multiplication and addition with scalar value\n", "print(\"\\n Matrix 2x+1 \\n\")\n", "print(2*x+1)" ] }, { "cell_type": "markdown", "metadata": { "id": "gOJXFR_zUwP7" }, "source": [ "Transform back to list of lists" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "q1qGD3noUwP7", "outputId": "e9666b1d-51f8-4474-a753-3784a76200e5" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[[np.int64(6), np.int64(7), np.int64(8)],\n", " [np.int64(9), np.int64(4), np.int64(8)]]" ] }, "metadata": {}, "execution_count": 93 } ], "source": [ "lx = [list(y) for y in x]\n", "lx" ] }, { "cell_type": "markdown", "metadata": { "id": "tf32p91OUwP8" }, "source": [ "**Pandas data frames**\n", "\n", "A data frame is a table in which each row and column is given a label. Very similar to a spreahsheet or a SQL table.\n", "\n", "Pandas DataFrames are documented at: http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.html\n", "\n", "Pandas dataframes enable different data analysis operations" ] }, { "cell_type": "markdown", "metadata": { "id": "EKwxhIBRUwP8" }, "source": [ "### Creating Data Frames\n", "\n", "A dataframe has names for the columns and the rows of the table. The column names are stored in the attribute **columns**, while the row names in the attribute **index**. When these are not speficied, they are just indexed by default with the numbers 0,1,...\n", "\n", "There are multiple ways we can create a data frame. Here we list just a few." ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "id": "1oy8RJTXUwP8" }, "outputs": [], "source": [ "import pandas as pd #The pandas library\n", "from pandas import Series, DataFrame #Main pandas data structures" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "id": "NOxE9MVqUwP8", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "e7da6edd-5225-4ea1-d645-a42fd9762ba5" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " 0 1 2\n", "0 1 2 3\n", "1 9 10 12\n" ] } ], "source": [ "#Creating a data frame from a list of lists\n", "\n", "df = pd.DataFrame([[1,2,3],[9,10,12]])\n", "print(df)\n", "\n", "# Each list becomes a row\n", "# Names of columns are 0,1,2\n", "# Rows are indexed by position numbers 0,1" ] }, { "cell_type": "code", "source": [ "df" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 125 }, "id": "fzNMLSxyzLE9", "outputId": "3d3962b7-d699-42f5-d47f-52ba444beee0" }, "execution_count": 96, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " 0 1 2\n", "0 1 2 3\n", "1 9 10 12" ], "text/html": [ "\n", "
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df", "summary": "{\n \"name\": \"df\",\n \"rows\": 2,\n \"fields\": [\n {\n \"column\": 0,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 1,\n \"max\": 9,\n \"num_unique_values\": 2,\n \"samples\": [\n 9,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 1,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 2,\n \"max\": 10,\n \"num_unique_values\": 2,\n \"samples\": [\n 10,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": 2,\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 6,\n \"min\": 3,\n \"max\": 12,\n \"num_unique_values\": 2,\n \"samples\": [\n 12,\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 96 } ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "id": "mYOrA6TcUwP9", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "18b01d98-1114-494e-e68d-94414700c83c" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " 0 1 2\n", "0 1 2 3\n", "1 9 10 12\n" ] } ], "source": [ "#Creating a data frame from a numpy array\n", "\n", "df = pd.DataFrame(np.array([[1,2,3],[9,10,12]]))\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "id": "PjTf-K2JUwP9", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "f2e787b9-905f-4f6c-e506-4007172d580e" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " A B C\n", "0 1 2 3\n", "1 9 10 12\n" ] } ], "source": [ "# Specifying column names\n", "df = pd.DataFrame(np.array([[1,2,3],[9,10,12]]), columns=['A','B','C'])\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "id": "XGEqMJE3UwP9", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "6b6262f0-c482-4818-944c-ac58d5242118" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " A B\n", "0 1 a\n", "1 2 b\n", "2 3 c\n" ] } ], "source": [ "#Creating a data frame from a dictionary\n", "# Keys are column names, values are lists with column values\n", "\n", "dfe = pd.DataFrame({'A':[1,2,3], 'B':['a','b','c']})\n", "print(dfe)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "id": "80Ak-NRBUwP-", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "231f2eed-cfd4-4395-cbd4-d8917a51eab0" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " NUMBER CHAR\n", "0 1 a\n", "1 2 b\n", "2 3 c\n", "Index(['NUMBER', 'CHAR'], dtype='object')\n", "['NUMBER', 'CHAR']\n" ] } ], "source": [ "# Reading from a csv file:\n", "df = pd.read_csv('example.csv') # Default is to \"think\" of a header at the CSV file\n", "print(df)\n", "\n", "# The first row of the file is used for the column names\n", "# The property columns gives us the column names\n", "print(df.columns)\n", "print(list(df.columns))" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "id": "imsKRcWjUwP-", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "1abdd89c-5e7e-4fe2-8709-7ad87c7c6bc3" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " 0 1\n", "0 1 a\n", "1 2 b\n", "2 3 c\n" ] } ], "source": [ "# Reading from a csv file without header:\n", "df = pd.read_csv('no-header.csv',header = None) # Change: header = None\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "id": "l7PdKdnQUwP-", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "f25e3819-5003-4891-eb26-dd2f013520bf" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " NUMBER CHAR\n", "0 1 a\n", "1 2 b\n", "2 3 c\n" ] } ], "source": [ "# Reading from an excel file:\n", "df = pd.read_excel('example.xlsx')\n", "print(df)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "id": "XmDNXVrSUwP-", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "af4ad1fd-4c2a-41af-942e-feda1bb6feff" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ ",NUMBER,CHAR\n", "0,1,a\n", "1,2,b\n", "2,3,c\n" ] } ], "source": [ "#Writing to a csv file:\n", "df.to_csv('example2.csv')\n", "for x in open('example2.csv').readlines():\n", " print(x.strip())" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "id": "jujeq4nUUwP_", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "9d93ab26-1f51-4925-d093-af694c7bd5f6" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "NUMBER,CHAR\n", "1,a\n", "2,b\n", "3,c\n" ] } ], "source": [ "# By default the row index is added as a column, we can remove it by seting index=False\n", "df.to_csv('example2.csv',index = False)\n", "for x in open('example2.csv').readlines():\n", " print(x.strip())" ] }, { "cell_type": "markdown", "metadata": { "id": "lY8zQTd5UwP_" }, "source": [ "***Fetching data***\n", "\n", "For demonstration purposes, we will use data from Tiingo on stock quotes. We will see two ways of fetching data from Tiingo, one using the Tiingo client and one using the Data Reader library of Pandas.\n", "\n", "More information on what types of data you can fetch is at:\n", "https://pandas-datareader.readthedocs.io/en/latest/remote_data.html\n", "\n", "\n", "We will use stock quotes from IEX. To make use of these you need to first create an account and obtain an API key. Then you set the environment variable IEX_API_KEY to the value of the key as it is snown below" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "id": "UtAXAajpUwP_" }, "outputs": [], "source": [ "from datetime import datetime #For handling dates" ] }, { "cell_type": "code", "source": [ "!pip install tiingo\n", "import os" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LGkmIJCcSDJa", "outputId": "69fe6cfb-9aaa-45da-d034-b8607ed95f2b" }, "execution_count": 106, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: tiingo in /usr/local/lib/python3.12/dist-packages (0.16.1)\n", "Requirement already satisfied: requests in /usr/local/lib/python3.12/dist-packages (from tiingo) (2.32.4)\n", "Requirement already satisfied: websocket-client in /usr/local/lib/python3.12/dist-packages (from tiingo) (1.9.0)\n", "Requirement already satisfied: charset_normalizer<4,>=2 in /usr/local/lib/python3.12/dist-packages (from requests->tiingo) (3.4.4)\n", "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.12/dist-packages (from requests->tiingo) (3.11)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.12/dist-packages (from requests->tiingo) (2.5.0)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.12/dist-packages (from requests->tiingo) (2025.10.5)\n" ] } ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "id": "a2YsLYczUwP_" }, "outputs": [], "source": [ "from tiingo import TiingoClient\n", "\n", "import pandas_datareader.data as web # For accessing web data\n", "\n", "client = TiingoClient({'api_key':'614c1590a592cc6696f6082f83b2666cd83882ef'})\n", "start = datetime(2018,1,1)\n", "end = datetime(2018,12,31)\n", "stocks_data = client.get_dataframe('META',frequency='daily',startDate=start,endDate=end)\n", "stocks_data = stocks_data[['open','close','low','high','volume']]" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "id": "IAkVH-O_UwP_", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "5d7c9e0b-84da-431f-c41d-8014f363f704" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "DatetimeIndex: 251 entries, 2018-01-02 00:00:00+00:00 to 2018-12-31 00:00:00+00:00\n", "Data columns (total 5 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 open 251 non-null float64\n", " 1 close 251 non-null float64\n", " 2 low 251 non-null float64\n", " 3 high 251 non-null float64\n", " 4 volume 251 non-null int64 \n", "dtypes: float64(4), int64(1)\n", "memory usage: 11.8 KB\n" ] } ], "source": [ "# the method info() outputs basic information for our data frame\n", "stocks_data.info()" ] }, { "cell_type": "markdown", "metadata": { "id": "GLuUpH5hUwQA" }, "source": [ "The number of rows in the DataFrame:" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "scrolled": true, "id": "dzBkYwSOUwQA", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "5fe638a4-13bf-4304-e743-3ef9824a79cc" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "251" ] }, "metadata": {}, "execution_count": 109 } ], "source": [ "len(stocks_data)" ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 237 }, "id": "MIkfIJgoUwQA", "outputId": "55c25c30-c1c7-4426-c16a-08fe5c77a30f" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " open close low high volume\n", "date \n", "2018-01-02 00:00:00+00:00 177.68 181.42 177.55 181.58 17694891\n", "2018-01-03 00:00:00+00:00 181.88 184.67 181.33 184.78 16595495\n", "2018-01-04 00:00:00+00:00 184.90 184.33 184.10 186.21 13554357\n", "2018-01-05 00:00:00+00:00 185.59 186.85 184.93 186.90 13042388\n", "2018-01-08 00:00:00+00:00 187.20 188.28 186.33 188.90 14719216" ], "text/html": [ "\n", "
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" ] }, "metadata": {}, "execution_count": 112 } ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 297 }, "id": "IIL02xDcUwQB", "outputId": "e32ab4d5-7b62-4517-e271-730c1e7e8678" }, "outputs": [ { "output_type": "error", "ename": "AttributeError", "evalue": "'DataFrame' object has no attribute 'date'", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipython-input-626017641.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#trying to access the date column will give an error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mstocks_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 6297\u001b[0m ):\n\u001b[1;32m 6298\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6299\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6301\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mfinal\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'date'" ] } ], "source": [ "#trying to access the date column will give an error\n", "\n", "stocks_data.date" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "scrolled": false, "colab": { "base_uri": "https://localhost:8080/" }, "id": "wFWvuUBtUwQB", "outputId": "074c857c-49ab-4cfe-8946-66acf3bb7669" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "date,open,close,low,high,volume\n", "2018-01-02 00:00:00+00:00,177.68,181.42,177.55,181.58,17694891\n", "2018-01-03 00:00:00+00:00,181.88,184.67,181.33,184.78,16595495\n", "2018-01-04 00:00:00+00:00,184.9,184.33,184.1,186.21,13554357\n", "2018-01-05 00:00:00+00:00,185.59,186.85,184.93,186.9,13042388\n", "2018-01-08 00:00:00+00:00,187.2,188.28,186.33,188.9,14719216\n", "2018-01-09 00:00:00+00:00,188.7,187.87,187.1,188.8,12342722\n", "2018-01-10 00:00:00+00:00,186.94,187.84,185.63,187.89,10464528\n", "2018-01-11 00:00:00+00:00,188.4,187.77,187.38,188.4,8855144\n", "2018-01-12 00:00:00+00:00,178.06,179.37,177.4,181.48,76645626\n" ] } ], "source": [ "stocks_data.to_csv('stocks_data.csv')\n", "for x in open('stocks_data.csv').readlines()[0:10]: # Read and print First 10 lines\n", " print(x.strip())\n", "df = pd.read_csv('stocks_data.csv')" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "scrolled": true, "colab": { "base_uri": "https://localhost:8080/" }, "id": "oXoQr3WOUwQG", "outputId": "b19d525a-5955-41fa-fee6-5f394ec2d9ae" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "251" ] }, "metadata": {}, "execution_count": 115 } ], "source": [ "len(df)" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "Do8OXqV3UwQH", "outputId": "63b07dde-5ac2-4a6a-8cc2-3fe8dd3bfe4f" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " date open close low high volume\n", "0 2018-01-02 00:00:00+00:00 177.68 181.42 177.55 181.58 17694891\n", "1 2018-01-03 00:00:00+00:00 181.88 184.67 181.33 184.78 16595495\n", "2 2018-01-04 00:00:00+00:00 184.90 184.33 184.10 186.21 13554357\n", "3 2018-01-05 00:00:00+00:00 185.59 186.85 184.93 186.90 13042388\n", "4 2018-01-08 00:00:00+00:00 187.20 188.28 186.33 188.90 14719216" ], "text/html": [ "\n", "
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" ] }, "metadata": {}, "execution_count": 117 } ] }, { "cell_type": "markdown", "metadata": { "id": "880gMlFLUwQH" }, "source": [ "Note that in the new dataframe, there is now a date column, while the index values are numbers 0,1,..." ] }, { "cell_type": "markdown", "metadata": { "id": "UGczRVgjUwQH" }, "source": [ "### Working with data columns" ] }, { "cell_type": "markdown", "metadata": { "id": "4F-7vrHjUwQH" }, "source": [ "The columns are the \"features\" in your data" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "id": "4R_NpxIgUwQI", "outputId": "cce251b8-c37c-4516-c059-888fe140339d", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Index(['date', 'open', 'close', 'low', 'high', 'volume'], dtype='object')" ] }, "metadata": {}, "execution_count": 118 } ], "source": [ "#an object that refers to the names of the columns\n", "df.columns" ] }, { "cell_type": "markdown", "metadata": { "id": "KFd-RHepUwQI" }, "source": [ "We can also assign a list to the columns property in order to change the attribute names.\n", "\n", "Alternatively, you can change the name of an attribute using rename:" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "id": "GckaR3g9UwQI", "colab": { "base_uri": "https://localhost:8080/", "height": 223 }, "outputId": "d8358b0c-8dfa-4b5d-a51d-d1c57dd98657" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "['date', 'open', 'close', 'low', 'high', 'V']\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " date open high low close vol\n", "0 2018-01-02 00:00:00+00:00 177.68 181.42 177.55 181.58 17694891\n", "1 2018-01-03 00:00:00+00:00 181.88 184.67 181.33 184.78 16595495\n", "2 2018-01-04 00:00:00+00:00 184.90 184.33 184.10 186.21 13554357\n", "3 2018-01-05 00:00:00+00:00 185.59 186.85 184.93 186.90 13042388\n", "4 2018-01-08 00:00:00+00:00 187.20 188.28 186.33 188.90 14719216" ], "text/html": [ "\n", "
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" ] }, "metadata": {}, "execution_count": 120 } ], "source": [ "df['open'].head()" ] }, { "cell_type": "markdown", "metadata": { "id": "1v5Je7kTUwQJ" }, "source": [ "Another way of selecting **a single column** from your data" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 241 }, "id": "tKu-n0WnUwQJ", "outputId": "c6a8d34c-cb5a-4b2b-b31e-a7d5e518930a" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0 177.68\n", "1 181.88\n", "2 184.90\n", "3 185.59\n", "4 187.20\n", "Name: open, dtype: float64" ], "text/html": [ "
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" ] }, "metadata": {}, "execution_count": 121 } ], "source": [ "df.open.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "3ox_mkMLUwQJ" }, "source": [ "Selecting multiple columns" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "id": "AmK6x71aUwQJ", "outputId": "9253dd52-978a-4d95-b7d4-082fb4a3332e", "colab": { "base_uri": "https://localhost:8080/", "height": 206 } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " open close\n", "0 177.68 181.58\n", "1 181.88 184.78\n", "2 184.90 186.21\n", "3 185.59 186.90\n", "4 187.20 188.90" ], "text/html": [ "\n", "
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"df[['open','close']]\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.7584039165581937,\n \"min\": 177.68,\n \"max\": 187.2,\n \"num_unique_values\": 5,\n \"samples\": [\n 181.88,\n 187.2,\n 184.9\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.727522685515189,\n \"min\": 181.58,\n \"max\": 188.9,\n \"num_unique_values\": 5,\n \"samples\": [\n 184.78,\n 188.9,\n 186.21\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 122 } ], "source": [ "df[['open','close']].head()" ] }, { "cell_type": "markdown", "metadata": { "id": "j_fsq7saUwQJ" }, "source": [ "We can use the values method to obtain the values of one or more attributes.\n", "It returns a numpy array. You can trasform it into a list, by applying the list() operator." ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fsKNHcmZUwQK", "outputId": "6e58cb41-64ea-4ed9-c086-058c5a3c23ec" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([177.68, 181.88, 184.9 , 185.59, 187.2 , 188.7 , 186.94, 188.4 ,\n", " 178.06, 181.5 ])" ] }, "metadata": {}, "execution_count": 123 } ], "source": [ "df.open.values[:10]" ] }, { "cell_type": "code", "source": [ "type(df.open.values[:10])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "AAWDOazTZd3v", "outputId": "773462af-112b-4681-9deb-f67d35bc8043" }, "execution_count": 124, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "numpy.ndarray" ] }, "metadata": {}, "execution_count": 124 } ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 203 }, "id": "ueAPggEIUwQK", "outputId": "bf33fe3f-662b-482a-c7b0-9305522d04ca" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "pandas.core.frame.DataFrame" ], "text/html": [ "
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pandas.core.frame.DataFrame
def __init__(data=None, index: Axes | None=None, columns: Axes | None=None, dtype: Dtype | None=None, copy: bool | None=None) -> None
/usr/local/lib/python3.12/dist-packages/pandas/core/frame.pyTwo-dimensional, size-mutable, potentially heterogeneous tabular data.\n",
              "\n",
              "Data structure also contains labeled axes (rows and columns).\n",
              "Arithmetic operations align on both row and column labels. Can be\n",
              "thought of as a dict-like container for Series objects. The primary\n",
              "pandas data structure.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\n",
              "    Dict can contain Series, arrays, constants, dataclass or list-like objects. If\n",
              "    data is a dict, column order follows insertion-order. If a dict contains Series\n",
              "    which have an index defined, it is aligned by its index. This alignment also\n",
              "    occurs if data is a Series or a DataFrame itself. Alignment is done on\n",
              "    Series/DataFrame inputs.\n",
              "\n",
              "    If data is a list of dicts, column order follows insertion-order.\n",
              "\n",
              "index : Index or array-like\n",
              "    Index to use for resulting frame. Will default to RangeIndex if\n",
              "    no indexing information part of input data and no index provided.\n",
              "columns : Index or array-like\n",
              "    Column labels to use for resulting frame when data does not have them,\n",
              "    defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\n",
              "    will perform column selection instead.\n",
              "dtype : dtype, default None\n",
              "    Data type to force. Only a single dtype is allowed. If None, infer.\n",
              "copy : bool or None, default None\n",
              "    Copy data from inputs.\n",
              "    For dict data, the default of None behaves like ``copy=True``.  For DataFrame\n",
              "    or 2d ndarray input, the default of None behaves like ``copy=False``.\n",
              "    If data is a dict containing one or more Series (possibly of different dtypes),\n",
              "    ``copy=False`` will ensure that these inputs are not copied.\n",
              "\n",
              "    .. versionchanged:: 1.3.0\n",
              "\n",
              "See Also\n",
              "--------\n",
              "DataFrame.from_records : Constructor from tuples, also record arrays.\n",
              "DataFrame.from_dict : From dicts of Series, arrays, or dicts.\n",
              "read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
              "read_table : Read general delimited file into DataFrame.\n",
              "read_clipboard : Read text from clipboard into DataFrame.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide <basics.dataframe>` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing DataFrame from a dictionary.\n",
              "\n",
              ">>> d = {'col1': [1, 2], 'col2': [3, 4]}\n",
              ">>> df = pd.DataFrame(data=d)\n",
              ">>> df\n",
              "   col1  col2\n",
              "0     1     3\n",
              "1     2     4\n",
              "\n",
              "Notice that the inferred dtype is int64.\n",
              "\n",
              ">>> df.dtypes\n",
              "col1    int64\n",
              "col2    int64\n",
              "dtype: object\n",
              "\n",
              "To enforce a single dtype:\n",
              "\n",
              ">>> df = pd.DataFrame(data=d, dtype=np.int8)\n",
              ">>> df.dtypes\n",
              "col1    int8\n",
              "col2    int8\n",
              "dtype: object\n",
              "\n",
              "Constructing DataFrame from a dictionary including Series:\n",
              "\n",
              ">>> d = {'col1': [0, 1, 2, 3], 'col2': pd.Series([2, 3], index=[2, 3])}\n",
              ">>> pd.DataFrame(data=d, index=[0, 1, 2, 3])\n",
              "   col1  col2\n",
              "0     0   NaN\n",
              "1     1   NaN\n",
              "2     2   2.0\n",
              "3     3   3.0\n",
              "\n",
              "Constructing DataFrame from numpy ndarray:\n",
              "\n",
              ">>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n",
              "...                    columns=['a', 'b', 'c'])\n",
              ">>> df2\n",
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6\n",
              "2  7  8  9\n",
              "\n",
              "Constructing DataFrame from a numpy ndarray that has labeled columns:\n",
              "\n",
              ">>> data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n",
              "...                 dtype=[("a", "i4"), ("b", "i4"), ("c", "i4")])\n",
              ">>> df3 = pd.DataFrame(data, columns=['c', 'a'])\n",
              "...\n",
              ">>> df3\n",
              "   c  a\n",
              "0  3  1\n",
              "1  6  4\n",
              "2  9  7\n",
              "\n",
              "Constructing DataFrame from dataclass:\n",
              "\n",
              ">>> from dataclasses import make_dataclass\n",
              ">>> Point = make_dataclass("Point", [("x", int), ("y", int)])\n",
              ">>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\n",
              "   x  y\n",
              "0  0  0\n",
              "1  0  3\n",
              "2  2  3\n",
              "\n",
              "Constructing DataFrame from Series/DataFrame:\n",
              "\n",
              ">>> ser = pd.Series([1, 2, 3], index=["a", "b", "c"])\n",
              ">>> df = pd.DataFrame(data=ser, index=["a", "c"])\n",
              ">>> df\n",
              "   0\n",
              "a  1\n",
              "c  3\n",
              "\n",
              ">>> df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"], columns=["x"])\n",
              ">>> df2 = pd.DataFrame(data=df1, index=["a", "c"])\n",
              ">>> df2\n",
              "   x\n",
              "a  1\n",
              "c  3
\n", " \n", "
" ] }, "metadata": {}, "execution_count": 125 } ], "source": [ "type(df[['open','close']])" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "scrolled": true, "colab": { "base_uri": "https://localhost:8080/" }, "id": "O36neK5CUwQK", "outputId": "918db928-090b-4418-9bbb-b844f8fd1e14" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "array([[177.68, 181.58],\n", " [181.88, 184.78],\n", " [184.9 , 186.21],\n", " [185.59, 186.9 ],\n", " [187.2 , 188.9 ],\n", " [188.7 , 188.8 ],\n", " [186.94, 187.89],\n", " [188.4 , 188.4 ],\n", " [178.06, 181.48],\n", " [181.5 , 181.75]])" ] }, "metadata": {}, "execution_count": 126 } ], "source": [ "df[['open','close']].values[:10]" ] }, { "cell_type": "code", "source": [ "type(df[['open','close']].values[:10])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9t5MaOVW1NtL", "outputId": "f836278a-9f63-44c7-9477-10376ab58b3c" }, "execution_count": 127, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "numpy.ndarray" ] }, "metadata": {}, "execution_count": 127 } ] }, { "cell_type": "markdown", "metadata": { "id": "ckFo35jnUwQK" }, "source": [ "## Data Frame methods" ] }, { "cell_type": "markdown", "metadata": { "id": "h2UPRDAbUwQK" }, "source": [ "A DataFrame object has many useful methods." ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 477 }, "id": "-iNnQuhfUwQL", "outputId": "17220f91-b9b6-406b-a726-74f92ce94d52" }, "outputs": [ { "output_type": "error", "ename": "TypeError", "evalue": "Could not convert ['2018-01-02 00:00:00+00:002018-01-03 00:00:00+00:002018-01-04 00:00:00+00:002018-01-05 00:00:00+00:002018-01-08 00:00:00+00:002018-01-09 00:00:00+00:002018-01-10 00:00:00+00:002018-01-11 00:00:00+00:002018-01-12 00:00:00+00:002018-01-16 00:00:00+00:002018-01-17 00:00:00+00:002018-01-18 00:00:00+00:002018-01-19 00:00:00+00:002018-01-22 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00:00:00+00:002018-11-06 00:00:00+00:002018-11-07 00:00:00+00:002018-11-08 00:00:00+00:002018-11-09 00:00:00+00:002018-11-12 00:00:00+00:002018-11-13 00:00:00+00:002018-11-14 00:00:00+00:002018-11-15 00:00:00+00:002018-11-16 00:00:00+00:002018-11-19 00:00:00+00:002018-11-20 00:00:00+00:002018-11-21 00:00:00+00:002018-11-23 00:00:00+00:002018-11-26 00:00:00+00:002018-11-27 00:00:00+00:002018-11-28 00:00:00+00:002018-11-29 00:00:00+00:002018-11-30 00:00:00+00:002018-12-03 00:00:00+00:002018-12-04 00:00:00+00:002018-12-06 00:00:00+00:002018-12-07 00:00:00+00:002018-12-10 00:00:00+00:002018-12-11 00:00:00+00:002018-12-12 00:00:00+00:002018-12-13 00:00:00+00:002018-12-14 00:00:00+00:002018-12-17 00:00:00+00:002018-12-18 00:00:00+00:002018-12-19 00:00:00+00:002018-12-20 00:00:00+00:002018-12-21 00:00:00+00:002018-12-24 00:00:00+00:002018-12-26 00:00:00+00:002018-12-27 00:00:00+00:002018-12-28 00:00:00+00:002018-12-31 00:00:00+00:00'] to numeric", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipython-input-3584231175.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#produces the mean of the columns/features\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36mmean\u001b[0;34m(self, axis, skipna, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m 11691\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11692\u001b[0m ):\n\u001b[0;32m> 11693\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m 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00:00:00+00:002018-01-19 00:00:00+00:002018-01-22 00:00:00+00:002018-01-23 00:00:00+00:002018-01-24 00:00:00+00:002018-01-25 00:00:00+00:002018-01-26 00:00:00+00:002018-01-29 00:00:00+00:002018-01-30 00:00:00+00:002018-01-31 00:00:00+00:002018-02-01 00:00:00+00:002018-02-02 00:00:00+00:002018-02-05 00:00:00+00:002018-02-06 00:00:00+00:002018-02-07 00:00:00+00:002018-02-08 00:00:00+00:002018-02-09 00:00:00+00:002018-02-12 00:00:00+00:002018-02-13 00:00:00+00:002018-02-14 00:00:00+00:002018-02-15 00:00:00+00:002018-02-16 00:00:00+00:002018-02-20 00:00:00+00:002018-02-21 00:00:00+00:002018-02-22 00:00:00+00:002018-02-23 00:00:00+00:002018-02-26 00:00:00+00:002018-02-27 00:00:00+00:002018-02-28 00:00:00+00:002018-03-01 00:00:00+00:002018-03-02 00:00:00+00:002018-03-05 00:00:00+00:002018-03-06 00:00:00+00:002018-03-07 00:00:00+00:002018-03-08 00:00:00+00:002018-03-09 00:00:00+00:002018-03-12 00:00:00+00:002018-03-13 00:00:00+00:002018-03-14 00:00:00+00:002018-03-15 00:00:00+00:002018-03-16 00:00:00+00:002018-03-19 00:00:00+00:002018-03-20 00:00:00+00:002018-03-21 00:00:00+00:002018-03-22 00:00:00+00:002018-03-23 00:00:00+00:002018-03-26 00:00:00+00:002018-..." ] } ], "source": [ "df.mean() #produces the mean of the columns/features" ] }, { "cell_type": "markdown", "metadata": { "id": "Zpt3vS58UwQL" }, "source": [ "For the date column we cannot calculate the mean. 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" ] }, "metadata": {}, "execution_count": 131 } ], "source": [ "df[['open','high','low','close']].sem() #produces the standard error of the mean of the columns/features" ] }, { "cell_type": "markdown", "source": [ "Reminder:" ], "metadata": { "id": "UTjtjmiY3N9_" } }, { "cell_type": "markdown", "source": [ 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)\n", "\n", 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)\n", "\n", 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], "metadata": { "id": "ZqOirxxB3C-O" } }, { "cell_type": "markdown", "metadata": { "id": "2PTKF5coUwQM" }, "source": [ "Here is a manual way to compute the confidence interval. What you need is the size of the sample, and the confidence value." ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "id": "y7qkJodTUwQM", "outputId": "4117470e-7e78-425d-d674-daaa0f96c247" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " CI lower end CI higher end\n", "open 169.024405 173.921491\n", "high 169.027487 173.994426\n", "low 166.807605 171.798651\n", "low 166.807605 171.798651\n", "close 171.198608 176.028085" ], "text/html": [ "\n", "
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" ] }, "metadata": {}, "execution_count": 133 } ], "source": [ "df[['open','high','low','low','close']].median() #produces the median of the columns/features" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "id": "H7o2XGJMUwQM", "outputId": "36541a9d-69c8-4155-b39b-021b67ffaa7a", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "np.float64(171.47294820717133)" ] }, "metadata": {}, "execution_count": 134 } ], "source": [ "df.open.mean()" ] }, { "cell_type": "markdown", "source": [ "What is the difference between Median and Mean?" ], "metadata": { "id": "U47tggQO3nn6" } }, { "cell_type": "markdown", "metadata": { "id": "khIycycgUwQN" }, "source": [ "Use describe to get all statistics for the data" ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "id": "-6Fvswd9UwQN", "colab": { "base_uri": "https://localhost:8080/", "height": 300 }, "outputId": "c41f7ec7-490b-4aa2-fac6-35d733074461" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " open close low high volume\n", "count 251.000000 251.000000 251.000000 251.000000 2.510000e+02\n", "mean 171.472948 171.510956 169.303128 173.613347 2.743828e+07\n", "std 19.696493 19.977452 20.074408 19.424564 1.910143e+07\n", "min 123.100000 124.060000 123.020000 129.740000 8.855144e+06\n", "25% 157.815000 157.915000 155.525000 160.745000 1.750763e+07\n", "50% 174.890000 174.700000 172.830000 176.980000 2.186093e+07\n", "75% 184.915000 185.270000 183.420000 186.450000 2.984990e+07\n", "max 215.720000 217.500000 214.270000 218.620000 1.698037e+08" ], "text/html": [ "\n", "
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"stocks_data\",\n \"rows\": 8,\n \"fields\": [\n {\n \"column\": \"open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 69.04241507230512,\n \"min\": 19.69649335473974,\n \"max\": 251.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 171.47294820717133,\n 174.89,\n 251.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 69.09271783453468,\n \"min\": 19.97745158627776,\n \"max\": 251.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 171.51095617529882,\n 174.7,\n 251.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 68.64136468080771,\n \"min\": 20.07440767348364,\n \"max\": 251.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 169.30312788844623,\n 172.83,\n 251.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"high\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 69.07659208637818,\n \"min\": 19.424564323437952,\n \"max\": 251.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 173.61334661354581,\n 176.98,\n 251.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 54602025.58402818,\n \"min\": 251.0,\n \"max\": 169803668.0,\n \"num_unique_values\": 8,\n \"samples\": [\n 27438275.310756974,\n 21860931.0,\n 251.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 135 } ], "source": [ "stocks_data.describe()" ] }, { "cell_type": "markdown", "metadata": { "id": "aR1kqoazUwQN" }, "source": [ "We can obtain the sum of the column entries using the sum operation" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "id": "OT-ZIH4oUwQN", "colab": { "base_uri": "https://localhost:8080/", "height": 241 }, "outputId": "11d6503c-d045-4d15-ecb8-3d2a148c7ccf" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "open 4.303971e+04\n", "close 4.304925e+04\n", "low 4.249509e+04\n", "high 4.357695e+04\n", "volume 6.887007e+09\n", "dtype: float64" ], "text/html": [ "
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open4.303971e+04
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" ] }, "metadata": {}, "execution_count": 136 } ], "source": [ "stocks_data.sum()" ] }, { "cell_type": "markdown", "metadata": { "id": "F2ru9Q23UwQO" }, "source": [ "The functions we have seen work on columns. We can apply them to rows as well by specifying the **axis** of the data.\n", "\n", "**axis = 0** refers to the index, and it means rows, and it is the default behavior\n", "\n", "**axis = 1** means columns\n", "\n", "It is confusing, but the axis refers to the axis along which we perform the operation. For example when we specify the axis for the sum method it means that we will do the summation over this axis. So if we sum over the 0-axis it means that we obtain the sum of the column values." ] }, { "cell_type": "code", "execution_count": 137, "metadata": { "id": "QTUk0bu5UwQO", "colab": { "base_uri": "https://localhost:8080/", "height": 287 }, "outputId": "0ee8fec1-7ced-49d2-edaf-dcc79c13abdb" }, "outputs": [ { "output_type": "error", "ename": "TypeError", "evalue": "can only concatenate str (not \"float\") to str", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipython-input-1459321664.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36msum\u001b[0;34m(self, axis, skipna, numeric_only, min_count, **kwargs)\u001b[0m\n\u001b[1;32m 11668\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11669\u001b[0m ):\n\u001b[0;32m> 11670\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_only\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11671\u001b[0m \u001b[0;32mreturn\u001b[0m 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\u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11483\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_get_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 83\u001b[0m )\n\u001b[1;32m 84\u001b[0m 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\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 403\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 404\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 406\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mdatetimelike\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mnewfunc\u001b[0;34m(values, axis, **kwargs)\u001b[0m\n\u001b[1;32m 475\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 476\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 477\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 478\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 479\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnewfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mnansum\u001b[0;34m(values, axis, skipna, min_count, mask)\u001b[0m\n\u001b[1;32m 644\u001b[0m \u001b[0mdtype_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 645\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 646\u001b[0;31m \u001b[0mthe_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_sum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 647\u001b[0m \u001b[0mthe_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_maybe_null_out\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthe_sum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmin_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmin_count\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 648\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/numpy/_core/_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m 50\u001b[0m def _sum(a, axis=None, dtype=None, out=None, keepdims=False,\n\u001b[1;32m 51\u001b[0m initial=_NoValue, where=True):\n\u001b[0;32m---> 52\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 53\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m def _prod(a, axis=None, dtype=None, out=None, keepdims=False,\n", "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"float\") to str" ] } ], "source": [ "df.sum(axis=1)" ] }, { "cell_type": "code", "source": [ "df[['open', 'close', 'low', 'high', 'vol']].sum(axis=1)" ], "metadata": { "id": "w7uEn6t6iP-y", "colab": { "base_uri": "https://localhost:8080/", "height": 458 }, "outputId": "cff1a623-9426-46c3-9bc2-4b2843d7bc93" }, "execution_count": 138, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0 17695609.23\n", "1 16596227.66\n", "2 13555096.54\n", "3 13043132.27\n", "4 14719966.71\n", " ... \n", "246 22066501.92\n", "247 39723890.31\n", "248 31203040.62\n", "249 22628105.66\n", "250 24625838.13\n", "Length: 251, dtype: float64" ], "text/html": [ "
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" ] }, "metadata": {}, "execution_count": 138 } ] }, { "cell_type": "markdown", "metadata": { "id": "Cno4Hqw8UwQO" }, "source": [ "**Sorting**: You can sort by a specific column, ascending (default) or descending. You can also sort inplace." ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "id": "yNNI7mcdUwQO", "colab": { "base_uri": "https://localhost:8080/", "height": 237 }, "outputId": "e8d17143-d25d-4c1b-e008-9c3cd74f38f0" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " open close low high volume\n", "date \n", "2018-07-25 00:00:00+00:00 215.72 217.50 214.27 218.62 64592585\n", "2018-07-24 00:00:00+00:00 215.11 214.67 212.60 216.20 28468681\n", "2018-07-23 00:00:00+00:00 210.58 210.91 208.80 211.62 16731969\n", "2018-07-18 00:00:00+00:00 209.82 209.36 208.44 210.99 15334907\n", "2018-07-20 00:00:00+00:00 208.85 209.94 208.50 211.50 16241508" ], "text/html": [ "\n", "
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opencloselowhighvolume
date
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"stocks_data\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2018-07-18 00:00:00+00:00\",\n \"max\": \"2018-07-25 00:00:00+00:00\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"2018-07-24 00:00:00+00:00\",\n \"2018-07-20 00:00:00+00:00\",\n \"2018-07-23 00:00:00+00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.170194000372852,\n \"min\": 208.85,\n \"max\": 215.72,\n \"num_unique_values\": 5,\n \"samples\": [\n 215.11,\n 208.85,\n 210.58\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.4873815392067398,\n \"min\": 209.36,\n \"max\": 217.5,\n \"num_unique_values\": 5,\n \"samples\": [\n 214.67,\n 209.94,\n 210.91\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.727365028741112,\n \"min\": 208.44,\n \"max\": 214.27,\n \"num_unique_values\": 5,\n \"samples\": [\n 212.6,\n 208.5,\n 208.8\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"high\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.425270792214826,\n \"min\": 210.99,\n \"max\": 218.62,\n \"num_unique_values\": 5,\n \"samples\": [\n 216.2,\n 211.5,\n 211.62\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 21002958,\n \"min\": 15334907,\n \"max\": 64592585,\n \"num_unique_values\": 5,\n \"samples\": [\n 28468681,\n 16241508,\n 16731969\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 139 } ], "source": [ "#when inplace is False (the default) it returns a NEW dataframe that is sorted.\n", "#when it is True it does not return anything, just changes the dataframe.\n", "stocks_data.sort_values(by = 'open', ascending =False, inplace=False).head()" ] }, { "cell_type": "markdown", "metadata": { "id": "vTBoKmyaUwQO" }, "source": [ "### Bulk Operations" ] }, { "cell_type": "markdown", "metadata": { "id": "vgb36_IJUwQP" }, "source": [ "Methods like **sum( )** and **std( )** work on entire columns.\n", "\n", "We can run our own functions across all values in a column (or row) using **apply( )**." ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "id": "AhhTq_DrUwQP", "colab": { "base_uri": "https://localhost:8080/", "height": 241 }, "outputId": "9f31bbb8-c6d9-4056-8e9e-1be03d37fb1e" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0 2018-01-02 00:00:00+00:00\n", "1 2018-01-03 00:00:00+00:00\n", "2 2018-01-04 00:00:00+00:00\n", "3 2018-01-05 00:00:00+00:00\n", "4 2018-01-08 00:00:00+00:00\n", "Name: date, dtype: object" ], "text/html": [ "
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date
02018-01-02 00:00:00+00:00
12018-01-03 00:00:00+00:00
22018-01-04 00:00:00+00:00
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" ] }, "metadata": {}, "execution_count": 140 } ], "source": [ "df.date.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "Mp5lH-AWUwQP" }, "source": [ "The **values** property of the column returns a numpy array of values for the column. Inspecting the first value reveals that these are strings with a particular format." ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "id": "3zb2XdfjUwQP", "colab": { "base_uri": "https://localhost:8080/", "height": 36 }, "outputId": "ea55dc4b-5208-4241-b57d-5a3f496c9059" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'2018-01-02 00:00:00+00:00'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 141 } ], "source": [ "first_date = df.date.values[0]\n", "first_date\n", "#returns a string" ] }, { "cell_type": "markdown", "metadata": { "id": "T-drOHBSUwQQ" }, "source": [ "The **datetime** library handles dates. The method strptime transforms a string into a date (according to a format given as parameter)." ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "id": "DzogT5QDUwQQ", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "b412a283-abd4-4b0f-94b6-703b6ad7653e" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "datetime.datetime(2018, 1, 2, 0, 0, tzinfo=datetime.timezone.utc)" ] }, "metadata": {}, "execution_count": 142 } ], "source": [ "datetime.strptime(first_date, \"%Y-%m-%d %H:%M:%S%z\")" ] }, { "cell_type": "markdown", "metadata": { "id": "xBYLk-MkUwQQ" }, "source": [ "We will now make use of two operations:\n", "\n", "The **apply** method takes a dataframe and applies a function that is given as input to apply to all the entries in the data frame. In the case below we apply it to just one column.\n", "\n", "The **lambda** function allows to define an anonymus function that takes some parameters (d) and uses them to compute some expression.\n", "\n", "Using the lambda function with apply, we can apply the function to all the entries of the data frame (in this case the column values)" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "id": "H2cVskZHUwQQ", "colab": { "base_uri": "https://localhost:8080/", "height": 241 }, "outputId": "65283ee1-9d31-4ab8-b835-11fda7d0cb10" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0 2018-01-02\n", "1 2018-01-03\n", "2 2018-01-04\n", "3 2018-01-05\n", "4 2018-01-08\n", "Name: date, dtype: object" ], "text/html": [ "
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" ] }, "metadata": {}, "execution_count": 143 } ], "source": [ "df.date = df.date.apply(lambda d: datetime.strptime(d, \"%Y-%m-%d %H:%M:%S%z\").strftime(\"%Y-%m-%d\"))\n", "date_series = df.date # We want to keep the dates\n", "df.date.head()\n", "\n", "#Another way to do the same thing, by applying the function to every row (axis = 1)\n", "#df.date = df.apply(lambda row: datetime.strptime(row.date, \"%Y-%m-%d\"), axis=1)" ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "id": "IGM9aMAtUwQR", "colab": { "base_uri": "https://localhost:8080/", "height": 241 }, "outputId": "48293832-5be1-4e23-dc70-fefc576dfba2" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0 2018-01-02\n", "1 2018-01-03\n", "2 2018-01-04\n", "3 2018-01-05\n", "4 2018-01-08\n", "Name: date, dtype: object" ], "text/html": [ "
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" ] }, "metadata": {}, "execution_count": 144 } ], "source": [ "date_series.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "G2GDz0brUwQR" }, "source": [ "For example, we can obtain the integer part of the open value" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "id": "Z1dhLyQuUwQR", "colab": { "base_uri": "https://localhost:8080/", "height": 458 }, "outputId": "0993a2f4-43ca-4ecd-fcf8-d4b8eea159a8" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0 177\n", "1 181\n", "2 184\n", "3 185\n", "4 187\n", " ... \n", "246 123\n", "247 126\n", "248 132\n", "249 135\n", "250 134\n", "Length: 251, dtype: int64" ], "text/html": [ "
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" ] }, "metadata": {}, "execution_count": 145 } ], "source": [ "#dftest = df[['open','close']]\n", "#dftest.apply(lambda x: int(x))\n", "df.apply(lambda r: int(r.open), axis=1)" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "id": "krM6YQqjUwQR", "colab": { "base_uri": "https://localhost:8080/", "height": 458 }, "outputId": "b31787f4-eaf1-42d4-f217-17c8d43c7948" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0 177\n", "1 181\n", "2 184\n", "3 185\n", "4 187\n", " ... \n", "246 123\n", "247 126\n", "248 132\n", "249 135\n", "250 134\n", "Name: open, Length: 251, dtype: int64" ], "text/html": [ "
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" ] }, "metadata": {}, "execution_count": 146 } ], "source": [ "dftest = df['open']\n", "dftest.apply(lambda x: int(x))" ] }, { "cell_type": "markdown", "metadata": { "id": "1RYqp9ZFUwQS" }, "source": [ "Each row in a DataFrame is associated with an index, which is a label that uniquely identifies a row." ] }, { "cell_type": "markdown", "metadata": { "id": "J2UC8OHXUwQS" }, "source": [ "The row indices so far have been auto-generated by pandas, and are simply integers starting from 0.\n", "\n", "From now on we will use dates instead of integers for indices -- the benefits of this will show later.\n", "\n", "Overwriting the index is as easy as assigning to the **`index`** property of the DataFrame." ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "id": "pLc_JrrJUwQS", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "a9dab229-905f-4574-be0a-230af0e48180" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" ] }, "metadata": {}, "execution_count": 147 } ], "source": [ "list(df.index)[0:10]" ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "id": "i7SsDAtsUwQS", "colab": { "base_uri": "https://localhost:8080/", "height": 237 }, "outputId": "14a9c092-5f36-45a7-e641-9b48d3ed36fb" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " date open high low close vol\n", "date \n", "2018-01-02 2018-01-02 177.68 181.42 177.55 181.58 17694891\n", "2018-01-03 2018-01-03 181.88 184.67 181.33 184.78 16595495\n", "2018-01-04 2018-01-04 184.90 184.33 184.10 186.21 13554357\n", "2018-01-05 2018-01-05 185.59 186.85 184.93 186.90 13042388\n", "2018-01-08 2018-01-08 187.20 188.28 186.33 188.90 14719216" ], "text/html": [ "\n", "
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "dfe", "repr_error": "cannot insert B, already exists" } }, "metadata": {}, "execution_count": 151 } ], "source": [ "dfe" ] }, { "cell_type": "markdown", "metadata": { "id": "2oM3kCPNUwQT" }, "source": [ "Now that we have made an index based on date, we can drop the original `date` column.\n", "We will not do it in this example to use it later on." ] }, { "cell_type": "code", "execution_count": 152, "metadata": { "id": "B6HnNKxoUwQT", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "4e0ee5e5-bf0c-49f7-81ef-0c278f081c55" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Index: 251 entries, 2018-01-02 to 2018-12-31\n", "Data columns (total 5 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 open 251 non-null float64\n", " 1 high 251 non-null float64\n", " 2 low 251 non-null float64\n", " 3 close 251 non-null float64\n", " 4 vol 251 non-null int64 \n", "dtypes: float64(4), int64(1)\n", "memory usage: 11.8+ KB\n" ] } ], "source": [ "df = df.drop(columns = ['date']) #Equivalent to df = df.drop(columns = ['date']), axis=1)\n", "df.info()" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "id": "Lx4VIaeYUwQT", "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "outputId": "2c3c1c21-a274-4ac5-be80-a30662684c5a" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " A B\n", "B \n", "a 1 a\n", "c 3 c" ], "text/html": [ "\n", "
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "repr_error": "cannot insert B, already exists" } }, "metadata": {}, "execution_count": 153 } ], "source": [ "#axis = 0 refers to dropping labels from rows (or you can use index = labels as a parameter).\n", "# Essentially we are droping a set of rows\n", "#axis = 1 refers to dropping labels from columns.\n", "dfe.drop(index='b')" ] }, { "cell_type": "markdown", "metadata": { "id": "FaXZcDtiUwQU" }, "source": [ "### Accessing rows of the DataFrame\n", "\n", "So far we've seen how to access a column of the DataFrame. To access a row we use a different notation.\n", "\n", "To access a row by its index value, use the **`.loc()`** method." ] }, { "cell_type": "code", "source": [ "df.index = pd.to_datetime(df.index)\n", "print(df.index)" ], "metadata": { "id": "slAY7DFEEdUS", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "b63c62c8-4ec4-4985-e21e-342dd1d76372" }, "execution_count": 154, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "DatetimeIndex(['2018-01-02', '2018-01-03', '2018-01-04', '2018-01-05',\n", " '2018-01-08', '2018-01-09', '2018-01-10', '2018-01-11',\n", " '2018-01-12', '2018-01-16',\n", " ...\n", " '2018-12-17', '2018-12-18', '2018-12-19', '2018-12-20',\n", " '2018-12-21', '2018-12-24', '2018-12-26', '2018-12-27',\n", " '2018-12-28', '2018-12-31'],\n", " dtype='datetime64[ns]', name='date', length=251, freq=None)\n" ] } ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "id": "73ehJQg8UwQU", "colab": { "base_uri": "https://localhost:8080/", "height": 241 }, "outputId": "b683c0ea-239b-4b76-e112-350cae68f002" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "open 177.35\n", "high 177.97\n", "low 177.17\n", "close 179.50\n", "vol 18697195.00\n", "Name: 2018-05-07 00:00:00, dtype: float64" ], "text/html": [ "
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" ] }, "metadata": {}, "execution_count": 155 } ], "source": [ "df.loc[datetime(2018,5,7)]" ] }, { "cell_type": "code", "source": [ "type(df.loc[datetime(2018,5,7)])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 203 }, "id": "68QAxK0V6k1a", "outputId": "46d5a31c-6c32-4034-e2ca-945596e4f087" }, "execution_count": 156, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "pandas.core.series.Series" ], "text/html": [ "
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pandas.core.series.Series
def __init__(data=None, index=None, dtype: Dtype | None=None, name=None, copy: bool | None=None, fastpath: bool | lib.NoDefault=lib.no_default) -> None
/usr/local/lib/python3.12/dist-packages/pandas/core/series.pyOne-dimensional ndarray with axis labels (including time series).\n",
              "\n",
              "Labels need not be unique but must be a hashable type. The object\n",
              "supports both integer- and label-based indexing and provides a host of\n",
              "methods for performing operations involving the index. Statistical\n",
              "methods from ndarray have been overridden to automatically exclude\n",
              "missing data (currently represented as NaN).\n",
              "\n",
              "Operations between Series (+, -, /, \\*, \\*\\*) align values based on their\n",
              "associated index values-- they need not be the same length. The result\n",
              "index will be the sorted union of the two indexes.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : array-like, Iterable, dict, or scalar value\n",
              "    Contains data stored in Series. If data is a dict, argument order is\n",
              "    maintained.\n",
              "index : array-like or Index (1d)\n",
              "    Values must be hashable and have the same length as `data`.\n",
              "    Non-unique index values are allowed. Will default to\n",
              "    RangeIndex (0, 1, 2, ..., n) if not provided. If data is dict-like\n",
              "    and index is None, then the keys in the data are used as the index. If the\n",
              "    index is not None, the resulting Series is reindexed with the index values.\n",
              "dtype : str, numpy.dtype, or ExtensionDtype, optional\n",
              "    Data type for the output Series. If not specified, this will be\n",
              "    inferred from `data`.\n",
              "    See the :ref:`user guide <basics.dtypes>` for more usages.\n",
              "name : Hashable, default None\n",
              "    The name to give to the Series.\n",
              "copy : bool, default False\n",
              "    Copy input data. Only affects Series or 1d ndarray input. See examples.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide <basics.series>` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing Series from a dictionary with an Index specified\n",
              "\n",
              ">>> d = {'a': 1, 'b': 2, 'c': 3}\n",
              ">>> ser = pd.Series(data=d, index=['a', 'b', 'c'])\n",
              ">>> ser\n",
              "a   1\n",
              "b   2\n",
              "c   3\n",
              "dtype: int64\n",
              "\n",
              "The keys of the dictionary match with the Index values, hence the Index\n",
              "values have no effect.\n",
              "\n",
              ">>> d = {'a': 1, 'b': 2, 'c': 3}\n",
              ">>> ser = pd.Series(data=d, index=['x', 'y', 'z'])\n",
              ">>> ser\n",
              "x   NaN\n",
              "y   NaN\n",
              "z   NaN\n",
              "dtype: float64\n",
              "\n",
              "Note that the Index is first build with the keys from the dictionary.\n",
              "After this the Series is reindexed with the given Index values, hence we\n",
              "get all NaN as a result.\n",
              "\n",
              "Constructing Series from a list with `copy=False`.\n",
              "\n",
              ">>> r = [1, 2]\n",
              ">>> ser = pd.Series(r, copy=False)\n",
              ">>> ser.iloc[0] = 999\n",
              ">>> r\n",
              "[1, 2]\n",
              ">>> ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `copy` of\n",
              "the original data even though `copy=False`, so\n",
              "the data is unchanged.\n",
              "\n",
              "Constructing Series from a 1d ndarray with `copy=False`.\n",
              "\n",
              ">>> r = np.array([1, 2])\n",
              ">>> ser = pd.Series(r, copy=False)\n",
              ">>> ser.iloc[0] = 999\n",
              ">>> r\n",
              "array([999,   2])\n",
              ">>> ser\n",
              "0    999\n",
              "1      2\n",
              "dtype: int64\n",
              "\n",
              "Due to input data type the Series has a `view` on\n",
              "the original data, so\n",
              "the data is changed as well.
\n", " \n", "
" ] }, "metadata": {}, "execution_count": 156 } ] }, { "cell_type": "markdown", "metadata": { "id": "BLhCawVMUwQU" }, "source": [ "To access a row by its sequence number (ie, like an array index), use **`.iloc[]`** ('Integer Location')" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "id": "AmRgc9H2UwQU", "colab": { "base_uri": "https://localhost:8080/", "height": 394 }, "outputId": "d6f0d018-ee4e-4a25-b290-38f8af0432a2" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " open high low close vol\n", "date \n", "2018-01-17 179.26 177.60 175.80 179.32 27356988\n", "2018-01-18 178.13 179.80 177.08 180.98 22783759\n", "2018-01-19 180.85 181.29 180.17 182.37 26266081\n", "2018-01-22 180.80 185.37 180.41 185.39 20567285\n", "2018-01-23 186.05 189.35 185.55 189.55 24956444\n", "2018-01-24 189.89 186.55 186.52 190.66 22992031\n", "2018-01-25 187.95 187.48 186.60 188.62 16698537\n", "2018-01-26 187.75 190.00 186.81 190.00 16652197\n", "2018-01-29 188.75 185.98 185.63 188.84 20022931\n", "2018-01-30 187.62 187.12 181.84 188.18 20382841" ], "text/html": [ "\n", "
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openhighlowclosevol
date
2018-01-17179.26177.60175.80179.3227356988
2018-01-18178.13179.80177.08180.9822783759
2018-01-19180.85181.29180.17182.3726266081
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2018-01-23186.05189.35185.55189.5524956444
2018-01-24189.89186.55186.52190.6622992031
2018-01-25187.95187.48186.60188.6216698537
2018-01-26187.75190.00186.81190.0016652197
2018-01-29188.75185.98185.63188.8420022931
2018-01-30187.62187.12181.84188.1820382841
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highclose
date
2018-01-02181.42181.58
2018-01-03184.67184.78
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highclose
date
2018-01-02181.42181.58
2018-01-03184.67184.78
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"df[['high','close']]\",\n \"rows\": 2,\n \"fields\": [\n {\n \"column\": \"date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2018-01-02 00:00:00\",\n \"max\": \"2018-01-03 00:00:00\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"2018-01-03 00:00:00\",\n \"2018-01-02 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"high\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.2980970388562794,\n \"min\": 181.42,\n \"max\": 184.67,\n \"num_unique_values\": 2,\n \"samples\": [\n 184.67,\n 181.42\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.262741699796944,\n \"min\": 181.58,\n \"max\": 184.78,\n \"num_unique_values\": 2,\n \"samples\": [\n 184.78,\n 181.58\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 159 } ], "source": [ "#select rows and columns\n", "df[['high','close']].iloc[0:2]" ] }, { "cell_type": "markdown", "metadata": { "id": "vM2SO58lUwQV" }, "source": [ "### To iterate over the rows, use **`.iterrows()`**" ] }, { "cell_type": "code", "source": [ "print(df.dtypes)" ], "metadata": { "id": "dmPH4cfoFlAo", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "7ac0d710-f418-4cd4-c32a-42a26a1d010c" }, "execution_count": 160, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "open float64\n", "high float64\n", "low float64\n", "close float64\n", "vol int64\n", "dtype: object\n" ] } ] }, { "cell_type": "code", "execution_count": 161, "metadata": { "id": "TbHQ7EaUUwQV", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "12d1d183-8766-4ff6-a954-1a33854668dc" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The total number of positive-gain days is 244.\n" ] } ], "source": [ "num_positive_days = 0\n", "for idx, row in df.iterrows(): #returns the index name and the row\n", " if row.close > row.open:\n", " num_positive_days += 1\n", "\n", "print(\"The total number of positive-gain days is {}.\".format(num_positive_days))" ] }, { "cell_type": "markdown", "metadata": { "id": "e5laFI9nUwQV" }, "source": [ "You can also do it this way:" ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "id": "GWQj_4E-UwQV", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "575829da-0451-4d67-dc42-48d4e71b932f" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The total number of positive-gain days is 244.\n" ] } ], "source": [ "num_positive_days = 0\n", "for i in range(len(df)):\n", " row = df.iloc[i]\n", " if row.close > row.open:\n", " num_positive_days += 1\n", "\n", "print(\"The total number of positive-gain days is {}.\".format(num_positive_days))" ] }, { "cell_type": "markdown", "metadata": { "id": "DCiMB0uAUwQV" }, "source": [ "Or this way:" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "id": "GNYZWrmqUwQV", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "a8ac5af5-c5fe-4f86-9a9d-740c828a5da1" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The total number of positive-gain days is 244\n" ] } ], "source": [ "pos_days = [idx for (idx,row) in df.iterrows() if row.close > row.open]\n", "print(\"The total number of positive-gain days is \"+str(len(pos_days)))" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "id": "deo2GKzbUwQW", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2185f1f5-cb3c-4e13-9612-c76cd6630b1c" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "[Timestamp('2018-01-02 00:00:00'),\n", " Timestamp('2018-01-03 00:00:00'),\n", " Timestamp('2018-01-04 00:00:00'),\n", " Timestamp('2018-01-05 00:00:00'),\n", " Timestamp('2018-01-08 00:00:00'),\n", " Timestamp('2018-01-09 00:00:00'),\n", " Timestamp('2018-01-10 00:00:00'),\n", " Timestamp('2018-01-12 00:00:00'),\n", " Timestamp('2018-01-16 00:00:00'),\n", " Timestamp('2018-01-17 00:00:00')]" ] }, "metadata": {}, "execution_count": 164 } ], "source": [ "pos_days[0:10]" ] }, { "cell_type": "code", "execution_count": 165, "metadata": { "id": "xoOzjqxfUwQW", "colab": { "base_uri": "https://localhost:8080/", "height": 455 }, "outputId": "8afbc872-fdc5-4fe1-be9e-866dbb9f7419" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " open high low close vol\n", "date \n", "2018-01-02 177.68 181.42 177.55 181.58 17694891\n", "2018-01-03 181.88 184.67 181.33 184.78 16595495\n", "2018-01-04 184.90 184.33 184.10 186.21 13554357\n", "2018-01-05 185.59 186.85 184.93 186.90 13042388\n", "2018-01-08 187.20 188.28 186.33 188.90 14719216\n", "... ... ... ... ... ...\n", "2018-12-24 123.10 124.06 123.02 129.74 22066002\n", "2018-12-26 126.00 134.18 125.89 134.24 39723370\n", "2018-12-27 132.44 134.52 129.67 134.99 31202509\n", "2018-12-28 135.34 133.20 132.20 135.92 22627569\n", "2018-12-31 134.45 131.09 129.95 134.64 24625308\n", "\n", "[244 rows x 5 columns]" ], "text/html": [ "\n", "
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openhighlowclosevol
date
2018-01-02177.68181.42177.55181.5817694891
2018-01-03181.88184.67181.33184.7816595495
2018-01-04184.90184.33184.10186.2113554357
2018-01-05185.59186.85184.93186.9013042388
2018-01-08187.20188.28186.33188.9014719216
..................
2018-12-24123.10124.06123.02129.7422066002
2018-12-26126.00134.18125.89134.2439723370
2018-12-27132.44134.52129.67134.9931202509
2018-12-28135.34133.20132.20135.9222627569
2018-12-31134.45131.09129.95134.6424625308
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openhighlowclosevol
date
2018-01-02177.68181.42177.55181.5817694891
2018-01-03181.88184.67181.33184.7816595495
2018-01-04184.90184.33184.10186.2113554357
2018-01-05185.59186.85184.93186.9013042388
2018-01-08187.20188.28186.33188.9014719216
..................
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2018-12-27132.44134.52129.67134.9931202509
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high
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" ] }, "metadata": {}, "execution_count": 169 } ], "source": [ "tmp_high = df.high > 170\n", "tmp_high" ] }, { "cell_type": "markdown", "metadata": { "id": "Mow6f7IwUwQZ" }, "source": [ "Summing a Boolean array is the same as counting the number of **`True`** values." ] }, { "cell_type": "code", "execution_count": 170, "metadata": { "id": "FOZgQzQMUwQZ", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "354b2086-6835-42fa-9102-fe1816b11b79" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "144" ] }, "metadata": {}, "execution_count": 170 } ], "source": [ "sum(tmp_high)" ] }, { "cell_type": "markdown", "metadata": { "id": "vn1XfWHcUwQZ" }, "source": [ "We can use the boolean dataframe to select the rows that have true value. The operation below returns only the rows of **`df`** that correspond to **`tmp_high`**" ] }, { "cell_type": "code", "execution_count": 171, "metadata": { "id": "BTX_rHjjUwQZ", "colab": { "base_uri": "https://localhost:8080/", "height": 455 }, "outputId": "c750631f-652a-4b77-86c0-71695a5fff22" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " open high low close vol\n", "date \n", "2018-01-02 177.68 181.42 177.55 181.58 17694891\n", "2018-01-03 181.88 184.67 181.33 184.78 16595495\n", "2018-01-04 184.90 184.33 184.10 186.21 13554357\n", "2018-01-05 185.59 186.85 184.93 186.90 13042388\n", "2018-01-08 187.20 188.28 186.33 188.90 14719216\n", "... ... ... ... ... ...\n", "2018-08-28 178.10 176.26 175.83 178.24 15910675\n", "2018-08-29 176.30 175.90 174.75 176.79 18678301\n", "2018-08-30 175.90 177.64 175.70 179.79 24216532\n", "2018-08-31 177.15 175.73 174.98 177.62 18065159\n", "2018-09-04 173.50 171.16 168.80 173.89 29808971\n", "\n", "[144 rows x 5 columns]" ], "text/html": [ "\n", "
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openhighlowclosevol
date
2018-01-02177.68181.42177.55181.5817694891
2018-01-03181.88184.67181.33184.7816595495
2018-01-04184.90184.33184.10186.2113554357
2018-01-05185.59186.85184.93186.9013042388
2018-01-08187.20188.28186.33188.9014719216
..................
2018-08-28178.10176.26175.83178.2415910675
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2018-08-30175.90177.64175.70179.7924216532
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openhighlowclosevol
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openhighlowclosevol
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2018-02-01188.22193.09187.89195.3253608910
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openhighlowclosevol
date
2018-03-20167.47168.15161.95170.20128925534
2018-03-21164.80169.39163.30173.40105350867
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2018-03-23165.44159.39159.02166.6052306891
2018-03-26160.82160.06149.02161.10125438294
..................
2018-12-24123.10124.06123.02129.7422066002
2018-12-26126.00134.18125.89134.2439723370
2018-12-27132.44134.52129.67134.9931202509
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openhighlowclosevol
date
2018-03-20167.47168.15161.95170.20128925534
2018-03-21164.80169.39163.30173.40105350867
2018-03-22166.13164.89163.72170.2773389988
2018-03-23165.44159.39159.02166.6052306891
2018-03-26160.82160.06149.02161.10125438294
..................
2018-12-24123.10124.06123.02129.7422066002
2018-12-26126.00134.18125.89134.2439723370
2018-12-27132.44134.52129.67134.9931202509
2018-12-28135.34133.20132.20135.9222627569
2018-12-31134.45131.09129.95134.6424625308
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openhighlowclosevolprofit
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" ] }, "metadata": {}, "execution_count": 178 } ] }, { "cell_type": "markdown", "source": [ "Let's now add another new column: Gain" ], "metadata": { "id": "qyuI9oyrebPI" } }, { "cell_type": "code", "execution_count": 179, "metadata": { "id": "EN7ShX2RUwQc", "colab": { "base_uri": "https://localhost:8080/", "height": 237 }, "outputId": "5a556b53-6086-44e9-cd43-0d1b9101767b" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " open high low close vol profit gain\n", "date \n", "2018-01-02 177.68 181.42 177.55 181.58 17694891 3.90 large_gain\n", "2018-01-03 181.88 184.67 181.33 184.78 16595495 2.90 medium_gain\n", "2018-01-04 184.90 184.33 184.10 186.21 13554357 1.31 medium_gain\n", "2018-01-05 185.59 186.85 184.93 186.90 13042388 1.31 medium_gain\n", "2018-01-08 187.20 188.28 186.33 188.90 14719216 1.70 medium_gain" ], "text/html": [ "\n", "
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openhighlowclosevolprofitgain
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2018-01-08187.20188.28186.33188.90147192161.70medium_gain
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openhighlowclosevolprofitgaintest_column
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df", "summary": "{\n \"name\": \"df\",\n \"rows\": 251,\n \"fields\": [\n {\n \"column\": \"date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2018-01-02 00:00:00\",\n \"max\": \"2018-12-31 00:00:00\",\n \"num_unique_values\": 251,\n \"samples\": [\n \"2018-08-14 00:00:00\",\n \"2018-01-10 00:00:00\",\n \"2018-08-27 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 19.69649335473974,\n \"min\": 123.1,\n \"max\": 215.72,\n \"num_unique_values\": 249,\n \"samples\": [\n 208.77,\n 186.94,\n 184.93\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"high\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 19.97745158627776,\n \"min\": 124.06,\n \"max\": 217.5,\n \"num_unique_values\": 249,\n \"samples\": [\n 209.94,\n 187.84,\n 186.9\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 20.07440767348364,\n \"min\": 123.02,\n \"max\": 214.27,\n \"num_unique_values\": 247,\n \"samples\": [\n 176.4,\n 185.63,\n 160.88\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 19.424564323437952,\n \"min\": 129.74,\n \"max\": 218.62,\n \"num_unique_values\": 246,\n \"samples\": [\n 177.95,\n 187.89,\n 171.77\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vol\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 19101434,\n \"min\": 8855144,\n \"max\": 169803668,\n \"num_unique_values\": 251,\n \"samples\": [\n 19101995,\n 10464528,\n 17921935\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"profit\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.844702426039888,\n \"min\": 0.0,\n \"max\": 8.599999999999994,\n \"num_unique_values\": 201,\n \"samples\": [\n 2.469999999999999,\n 0.6700000000000159,\n 0.8899999999999864\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gain\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"large_gain\",\n \"medium_gain\",\n \"small_gain\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"test_column\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"large_gain\",\n \"medium_gain\",\n \"small_gain\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 181 } ], "source": [ "df.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "XmrmzLrTUwQd" }, "source": [ "OK, point made, let's get rid of that extraneous `test_column`:" ] }, { "cell_type": "code", "execution_count": 182, "metadata": { "id": "ZZjCx8OmUwQd", "colab": { "base_uri": "https://localhost:8080/", "height": 237 }, "outputId": "916ea17b-9f6e-4228-c0fc-610ebdfcc773" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " open high low close vol profit gain\n", "date \n", "2018-01-02 177.68 181.42 177.55 181.58 17694891 3.90 large_gain\n", "2018-01-03 181.88 184.67 181.33 184.78 16595495 2.90 medium_gain\n", "2018-01-04 184.90 184.33 184.10 186.21 13554357 1.31 medium_gain\n", "2018-01-05 185.59 186.85 184.93 186.90 13042388 1.31 medium_gain\n", "2018-01-08 187.20 188.28 186.33 188.90 14719216 1.70 medium_gain" ], "text/html": [ "\n", "
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openhighlowclosevolprofitgain
date
2018-01-02177.68181.42177.55181.58176948913.90large_gain
2018-01-03181.88184.67181.33184.78165954952.90medium_gain
2018-01-04184.90184.33184.10186.21135543571.31medium_gain
2018-01-05185.59186.85184.93186.90130423881.31medium_gain
2018-01-08187.20188.28186.33188.90147192161.70medium_gain
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df", "summary": "{\n \"name\": \"df\",\n \"rows\": 251,\n \"fields\": [\n {\n \"column\": \"date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2018-01-02 00:00:00\",\n \"max\": \"2018-12-31 00:00:00\",\n \"num_unique_values\": 251,\n \"samples\": [\n \"2018-08-14 00:00:00\",\n \"2018-01-10 00:00:00\",\n \"2018-08-27 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 19.69649335473974,\n \"min\": 123.1,\n \"max\": 215.72,\n \"num_unique_values\": 249,\n \"samples\": [\n 208.77,\n 186.94,\n 184.93\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"high\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 19.97745158627776,\n \"min\": 124.06,\n \"max\": 217.5,\n \"num_unique_values\": 249,\n \"samples\": [\n 209.94,\n 187.84,\n 186.9\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 20.07440767348364,\n \"min\": 123.02,\n \"max\": 214.27,\n \"num_unique_values\": 247,\n \"samples\": [\n 176.4,\n 185.63,\n 160.88\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 19.424564323437952,\n \"min\": 129.74,\n \"max\": 218.62,\n \"num_unique_values\": 246,\n \"samples\": [\n 177.95,\n 187.89,\n 171.77\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vol\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 19101434,\n \"min\": 8855144,\n \"max\": 169803668,\n \"num_unique_values\": 251,\n \"samples\": [\n 19101995,\n 10464528,\n 17921935\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"profit\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.844702426039888,\n \"min\": 0.0,\n \"max\": 8.599999999999994,\n \"num_unique_values\": 201,\n \"samples\": [\n 2.469999999999999,\n 0.6700000000000159,\n 0.8899999999999864\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gain\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"large_gain\",\n \"medium_gain\",\n \"small_gain\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 182 } ], "source": [ "df = df.drop('test_column', axis = 1)\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": { "id": "L1fYLse-UwQe" }, "source": [ "### Missing values\n", "\n", "Data often has missing values. In Pandas these are denoted as NaN values. These may be part of our data (e.g. empty cells in an excel sheet), or they may appear as a result of a join. There are special methods for handling these values." ] }, { "cell_type": "code", "execution_count": 183, "metadata": { "id": "kFyixJOoUwQe", "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "outputId": "c601cd29-4893-417a-c127-df966eb688d1" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " A B C\n", "0 1.0 a x\n", "1 5.0 b NaN\n", "2 3.0 c y\n", "3 9.0 NaN z\n", "4 NaN a x" ], "text/html": [ "\n", "
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "mdf", "summary": "{\n \"name\": \"mdf\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"A\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.415650255319866,\n \"min\": 1.0,\n \"max\": 9.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 5.0,\n 9.0,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"B\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"a\",\n \"b\",\n \"c\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"C\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"x\",\n \"y\",\n \"z\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 183 } ], "source": [ "mdf = pd.read_csv('example-missing.csv')\n", "mdf" ] }, { "cell_type": "code", "execution_count": 184, "metadata": { "id": "ItRWrq9zUwQe", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "a3740a7b-3244-4dda-fef3-63a8a22f095c" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "np.float64(4.5)" ] }, "metadata": {}, "execution_count": 184 } ], "source": [ "mdf.A.mean()" ] }, { "cell_type": "markdown", "metadata": { "id": "SKU4j28qUwQf" }, "source": [ "We can fill the values using the fillna method" ] }, { "cell_type": "code", "execution_count": 185, "metadata": { "id": "-vDVXYKcUwQf", "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "outputId": "c2a724ca-35f4-4b1f-996c-d8c86e4e1fe0" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " A B C\n", "0 1.0 a x\n", "1 5.0 b 0\n", "2 3.0 c y\n", "3 9.0 0 z\n", "4 0.0 a x" ], "text/html": [ "\n", "
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "mdf", "summary": "{\n \"name\": \"mdf\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"A\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.958039891549808,\n \"min\": 1.0,\n \"max\": 9.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 5.0,\n 4.5,\n 3.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"B\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"b\",\n \"\",\n \"a\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"C\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 4,\n \"samples\": [\n \"\",\n \"z\",\n \"x\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 187 } ], "source": [ "mdf.A = mdf.A.fillna(mdf.A.mean()) # Instead of NaN put the mean\n", "mdf = mdf.fillna('') # Instead of NaN put the empty string\n", "mdf" ] }, { "cell_type": "markdown", "metadata": { "id": "SDTlMFI5UwQg" }, "source": [ "We can drop the rows with missing values" ] }, { "cell_type": "code", "execution_count": 188, "metadata": { "id": "7soUl4BdUwQg", "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "outputId": "508c8a07-a25c-424d-aadf-03d3cfe27445" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " A B C\n", "0 1.0 a x\n", "2 3.0 c y" ], "text/html": [ "\n", "
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"mdf\",\n \"rows\": 2,\n \"fields\": [\n {\n \"column\": \"A\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.4142135623730951,\n \"min\": 1.0,\n \"max\": 3.0,\n \"num_unique_values\": 2,\n \"samples\": [\n 3.0,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"B\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"c\",\n \"a\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"C\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"y\",\n \"x\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 188 } ], "source": [ "mdf = pd.read_csv('example-missing.csv')\n", "mdf.dropna()" ] }, { "cell_type": "markdown", "metadata": { "id": "_qNz9heUUwQg" }, "source": [ "We can find those rows" ] }, { "cell_type": "code", "execution_count": 189, "metadata": { "id": "sAs0AuzZUwQh", "colab": { "base_uri": "https://localhost:8080/", "height": 81 }, "outputId": "262f3453-523d-4e3e-ea23-aa23669687ee" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " A B C\n", "3 9.0 NaN z" ], "text/html": [ "\n", "
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ABC
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "repr_error": "0" } }, "metadata": {}, "execution_count": 189 } ], "source": [ "mdf[mdf.B.isnull()]" ] }, { "cell_type": "markdown", "metadata": { "id": "Qq-1zCEEUwQh" }, "source": [ "## Grouping" ] }, { "cell_type": "markdown", "metadata": { "id": "HEXynfqWUwQh" }, "source": [ "An **extremely** powerful DataFrame method is **`groupby()`**.\n", "\n", "This is entirely analagous to **`GROUP BY`** in SQL.\n", "\n", "It will group the rows of a DataFrame by the values in one (or more) columns, and let you iterate through each group." ] }, { "cell_type": "markdown", "metadata": { "id": "4LDtXf63UwQi" }, "source": [ "Here we will look at the average gain among the categories of gains (negative, small, medium and large) we defined above and stored in column `gain`." ] }, { "cell_type": "code", "source": [ "df" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 455 }, "id": "sIKAfKYKg0tF", "outputId": "932bcb09-9ba8-48b9-ce75-df95ae37c559" }, "execution_count": 190, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " open high low close vol profit gain\n", "date \n", "2018-01-02 177.68 181.42 177.55 181.58 17694891 3.90 large_gain\n", "2018-01-03 181.88 184.67 181.33 184.78 16595495 2.90 medium_gain\n", "2018-01-04 184.90 184.33 184.10 186.21 13554357 1.31 medium_gain\n", "2018-01-05 185.59 186.85 184.93 186.90 13042388 1.31 medium_gain\n", "2018-01-08 187.20 188.28 186.33 188.90 14719216 1.70 medium_gain\n", "... ... ... ... ... ... ... ...\n", "2018-12-24 123.10 124.06 123.02 129.74 22066002 6.64 large_gain\n", "2018-12-26 126.00 134.18 125.89 134.24 39723370 8.24 large_gain\n", "2018-12-27 132.44 134.52 129.67 134.99 31202509 2.55 medium_gain\n", "2018-12-28 135.34 133.20 132.20 135.92 22627569 0.58 small_gain\n", "2018-12-31 134.45 131.09 129.95 134.64 24625308 0.19 small_gain\n", "\n", "[251 rows x 7 columns]" ], "text/html": [ "\n", "
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2018-12-27132.44134.52129.67134.99312025092.55medium_gain
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pandas.core.groupby.generic.DataFrameGroupBy
def __init__(obj: NDFrameT, keys: _KeysArgType | None=None, axis: Axis=0, level: IndexLabel | None=None, grouper: ops.BaseGrouper | None=None, exclusions: frozenset[Hashable] | None=None, selection: IndexLabel | None=None, as_index: bool=True, sort: bool=True, group_keys: bool=True, observed: bool | lib.NoDefault=lib.no_default, dropna: bool=True) -> None
/usr/local/lib/python3.12/dist-packages/pandas/core/groupby/generic.pyClass for grouping and aggregating relational data.\n",
              "\n",
              "See aggregate, transform, and apply functions on this object.\n",
              "\n",
              "It's easiest to use obj.groupby(...) to use GroupBy, but you can also do:\n",
              "\n",
              "::\n",
              "\n",
              "    grouped = groupby(obj, ...)\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "obj : pandas object\n",
              "axis : int, default 0\n",
              "level : int, default None\n",
              "    Level of MultiIndex\n",
              "groupings : list of Grouping objects\n",
              "    Most users should ignore this\n",
              "exclusions : array-like, optional\n",
              "    List of columns to exclude\n",
              "name : str\n",
              "    Most users should ignore this\n",
              "\n",
              "Returns\n",
              "-------\n",
              "**Attributes**\n",
              "groups : dict\n",
              "    {group name -> group labels}\n",
              "len(grouped) : int\n",
              "    Number of groups\n",
              "\n",
              "Notes\n",
              "-----\n",
              "After grouping, see aggregate, apply, and transform functions. Here are\n",
              "some other brief notes about usage. When grouping by multiple groups, the\n",
              "result index will be a MultiIndex (hierarchical) by default.\n",
              "\n",
              "Iteration produces (key, group) tuples, i.e. chunking the data by group. So\n",
              "you can write code like:\n",
              "\n",
              "::\n",
              "\n",
              "    grouped = obj.groupby(keys, axis=axis)\n",
              "    for key, group in grouped:\n",
              "        # do something with the data\n",
              "\n",
              "Function calls on GroupBy, if not specially implemented, "dispatch" to the\n",
              "grouped data. So if you group a DataFrame and wish to invoke the std()\n",
              "method on each group, you can simply do:\n",
              "\n",
              "::\n",
              "\n",
              "    df.groupby(mapper).std()\n",
              "\n",
              "rather than\n",
              "\n",
              "::\n",
              "\n",
              "    df.groupby(mapper).aggregate(np.std)\n",
              "\n",
              "You can pass arguments to these "wrapped" functions, too.\n",
              "\n",
              "See the online documentation for full exposition on these topics and much\n",
              "more
\n", " \n", "
" ] }, "metadata": {}, "execution_count": 192 } ], "source": [ "type(gain_groups) #DataFrameGroupBy Object" ] }, { "cell_type": "markdown", "metadata": { "id": "MuO0PWH1UwQi" }, "source": [ "Essentially, **`gain_groups`** behaves like a dictionary\n", "* The keys are the unique values found in the `gain` column, and\n", "* The values are DataFrames that contain only the rows having the corresponding unique values." ] }, { "cell_type": "code", "execution_count": 193, "metadata": { "id": "2FaFdvTAUwQi", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "ff03d38d-29c3-4994-b4b2-e1899a21175a" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "large_gain\n", " open high low close vol profit gain\n", "date \n", "2018-01-02 177.68 181.42 177.55 181.58 17694891 3.90 large_gain\n", "2018-01-12 178.06 179.37 177.40 181.48 76645626 3.42 large_gain\n", "2018-01-22 180.80 185.37 180.41 185.39 20567285 4.59 large_gain\n", "2018-01-23 186.05 189.35 185.55 189.55 24956444 3.50 large_gain\n", "2018-02-01 188.22 193.09 187.89 195.32 53608910 7.10 large_gain\n", "=============================\n", "medium_gain\n", " open high low close vol profit gain\n", "date \n", "2018-01-03 181.88 184.67 181.33 184.78 16595495 2.90 medium_gain\n", "2018-01-04 184.90 184.33 184.10 186.21 13554357 1.31 medium_gain\n", "2018-01-05 185.59 186.85 184.93 186.90 13042388 1.31 medium_gain\n", "2018-01-08 187.20 188.28 186.33 188.90 14719216 1.70 medium_gain\n", "2018-01-18 178.13 179.80 177.08 180.98 22783759 2.85 medium_gain\n", "=============================\n", "small_gain\n", " open high low close vol profit gain\n", "date \n", "2018-01-09 188.70 187.87 187.10 188.80 12342722 0.10 small_gain\n", "2018-01-10 186.94 187.84 185.63 187.89 10464528 0.95 small_gain\n", "2018-01-11 188.40 187.77 187.38 188.40 8855144 0.00 small_gain\n", "2018-01-16 181.50 178.39 178.04 181.75 35027166 0.25 small_gain\n", "2018-01-17 179.26 177.60 175.80 179.32 27356988 0.06 small_gain\n", "=============================\n" ] } ], "source": [ "for gain, gain_data in gain_groups:\n", " print(gain)\n", " print(gain_data.head())\n", " print('=============================')" ] }, { "cell_type": "markdown", "metadata": { "id": "Ep7US835UwQj" }, "source": [ "We can obtain the dataframe that corresponds to a specific group by using the get_group method of the groupby object" ] }, { "cell_type": "code", "execution_count": 194, "metadata": { "id": "wNiHQzFWUwQj", "colab": { "base_uri": "https://localhost:8080/", "height": 237 }, "outputId": "b29bb6d6-be41-43dd-feb7-093c7eec53b6" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " open high low close vol profit gain\n", "date \n", "2018-01-09 188.70 187.87 187.10 188.80 12342722 0.10 small_gain\n", "2018-01-10 186.94 187.84 185.63 187.89 10464528 0.95 small_gain\n", "2018-01-11 188.40 187.77 187.38 188.40 8855144 0.00 small_gain\n", "2018-01-16 181.50 178.39 178.04 181.75 35027166 0.25 small_gain\n", "2018-01-17 179.26 177.60 175.80 179.32 27356988 0.06 small_gain" ], "text/html": [ "\n", "
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openhighlowclosevolprofitgain
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "sm", "summary": "{\n \"name\": \"sm\",\n \"rows\": 86,\n \"fields\": [\n {\n \"column\": \"date\",\n \"properties\": {\n \"dtype\": \"date\",\n \"min\": \"2018-01-09 00:00:00\",\n \"max\": \"2018-12-31 00:00:00\",\n \"num_unique_values\": 86,\n \"samples\": [\n \"2018-11-12 00:00:00\",\n \"2018-01-09 00:00:00\",\n \"2018-10-18 00:00:00\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 18.23272302347867,\n \"min\": 133.65,\n \"max\": 207.81,\n \"num_unique_values\": 85,\n \"samples\": [\n 135.75,\n 188.7,\n 159.56\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"high\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 18.67430890203266,\n \"min\": 131.09,\n \"max\": 207.32,\n \"num_unique_values\": 85,\n \"samples\": [\n 135.0,\n 187.87,\n 159.42\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 18.93782481355932,\n \"min\": 129.95,\n \"max\": 206.45,\n \"num_unique_values\": 85,\n \"samples\": [\n 133.71,\n 187.1,\n 157.95\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 18.25262376806701,\n \"min\": 134.5,\n \"max\": 208.43,\n \"num_unique_values\": 85,\n \"samples\": [\n 136.61,\n 188.8,\n 160.49\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vol\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 16667977,\n \"min\": 8855144,\n \"max\": 125438294,\n \"num_unique_values\": 86,\n \"samples\": [\n 18542123,\n 12342722,\n 21675084\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"profit\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.30722813101505525,\n \"min\": 0.0,\n \"max\": 0.9900000000000091,\n \"num_unique_values\": 56,\n \"samples\": [\n 0.10000000000002274,\n 0.7700000000000102,\n 0.6200000000000045\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gain\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1,\n \"samples\": [\n \"small_gain\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 194 } ], "source": [ "sm = gain_groups.get_group('small_gain')\n", "sm.head()" ] }, { "cell_type": "code", "execution_count": 195, "metadata": { "id": "hqpLIt5_UwQj", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "40ccced4-d512-4453-9feb-b7583947d064" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "The average closing value for the large_gain group is 171.15434782608693\n", "The average closing value for the medium_gain group is 175.6158333333333\n", "The average closing value for the small_gain group is 173.3509302325581\n" ] } ], "source": [ "for gain, gain_data in df.groupby(\"gain\"):\n", " print('The average closing value for the {} group is {}'.format(gain,\n", " gain_data.close.mean()))" ] }, { "cell_type": "markdown", "metadata": { "id": "-u9vn_BzUwQj" }, "source": [ "The operation above can be done with a typical SQL-like group by, where we group by one or more attributes, and **aggreagate** the values (of some) of the other attributes.\n", "\n", "For example group by \"gain\" and take the average of the values for open, high, low, close, volume.\n", "\n", "Example SQL code:\n", "SELECT gain, AVG(close) AS avg_close\n", "FROM stock_data\n", "GROUP BY gain;\n", "\n", "You can also use other aggregators such as count, sum, median, max, min.\n", "\n", "Pandas is now returning a new dataframe indexed by the values of the group-by attribute(s), with columns the other attributes" ] }, { "cell_type": "code", "execution_count": 196, "metadata": { "id": "potrFWP0UwQj", "colab": { "base_uri": "https://localhost:8080/", "height": 203 }, "outputId": "0ee95fbd-584d-41ca-a264-38105ab72a4e" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "pandas.core.frame.DataFrame" ], "text/html": [ "
\n", " 
pandas.core.frame.DataFrame
def __init__(data=None, index: Axes | None=None, columns: Axes | None=None, dtype: Dtype | None=None, copy: bool | None=None) -> None
/usr/local/lib/python3.12/dist-packages/pandas/core/frame.pyTwo-dimensional, size-mutable, potentially heterogeneous tabular data.\n",
              "\n",
              "Data structure also contains labeled axes (rows and columns).\n",
              "Arithmetic operations align on both row and column labels. Can be\n",
              "thought of as a dict-like container for Series objects. The primary\n",
              "pandas data structure.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\n",
              "    Dict can contain Series, arrays, constants, dataclass or list-like objects. If\n",
              "    data is a dict, column order follows insertion-order. If a dict contains Series\n",
              "    which have an index defined, it is aligned by its index. This alignment also\n",
              "    occurs if data is a Series or a DataFrame itself. Alignment is done on\n",
              "    Series/DataFrame inputs.\n",
              "\n",
              "    If data is a list of dicts, column order follows insertion-order.\n",
              "\n",
              "index : Index or array-like\n",
              "    Index to use for resulting frame. Will default to RangeIndex if\n",
              "    no indexing information part of input data and no index provided.\n",
              "columns : Index or array-like\n",
              "    Column labels to use for resulting frame when data does not have them,\n",
              "    defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\n",
              "    will perform column selection instead.\n",
              "dtype : dtype, default None\n",
              "    Data type to force. Only a single dtype is allowed. If None, infer.\n",
              "copy : bool or None, default None\n",
              "    Copy data from inputs.\n",
              "    For dict data, the default of None behaves like ``copy=True``.  For DataFrame\n",
              "    or 2d ndarray input, the default of None behaves like ``copy=False``.\n",
              "    If data is a dict containing one or more Series (possibly of different dtypes),\n",
              "    ``copy=False`` will ensure that these inputs are not copied.\n",
              "\n",
              "    .. versionchanged:: 1.3.0\n",
              "\n",
              "See Also\n",
              "--------\n",
              "DataFrame.from_records : Constructor from tuples, also record arrays.\n",
              "DataFrame.from_dict : From dicts of Series, arrays, or dicts.\n",
              "read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
              "read_table : Read general delimited file into DataFrame.\n",
              "read_clipboard : Read text from clipboard into DataFrame.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide <basics.dataframe>` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing DataFrame from a dictionary.\n",
              "\n",
              ">>> d = {'col1': [1, 2], 'col2': [3, 4]}\n",
              ">>> df = pd.DataFrame(data=d)\n",
              ">>> df\n",
              "   col1  col2\n",
              "0     1     3\n",
              "1     2     4\n",
              "\n",
              "Notice that the inferred dtype is int64.\n",
              "\n",
              ">>> df.dtypes\n",
              "col1    int64\n",
              "col2    int64\n",
              "dtype: object\n",
              "\n",
              "To enforce a single dtype:\n",
              "\n",
              ">>> df = pd.DataFrame(data=d, dtype=np.int8)\n",
              ">>> df.dtypes\n",
              "col1    int8\n",
              "col2    int8\n",
              "dtype: object\n",
              "\n",
              "Constructing DataFrame from a dictionary including Series:\n",
              "\n",
              ">>> d = {'col1': [0, 1, 2, 3], 'col2': pd.Series([2, 3], index=[2, 3])}\n",
              ">>> pd.DataFrame(data=d, index=[0, 1, 2, 3])\n",
              "   col1  col2\n",
              "0     0   NaN\n",
              "1     1   NaN\n",
              "2     2   2.0\n",
              "3     3   3.0\n",
              "\n",
              "Constructing DataFrame from numpy ndarray:\n",
              "\n",
              ">>> df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n",
              "...                    columns=['a', 'b', 'c'])\n",
              ">>> df2\n",
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6\n",
              "2  7  8  9\n",
              "\n",
              "Constructing DataFrame from a numpy ndarray that has labeled columns:\n",
              "\n",
              ">>> data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n",
              "...                 dtype=[("a", "i4"), ("b", "i4"), ("c", "i4")])\n",
              ">>> df3 = pd.DataFrame(data, columns=['c', 'a'])\n",
              "...\n",
              ">>> df3\n",
              "   c  a\n",
              "0  3  1\n",
              "1  6  4\n",
              "2  9  7\n",
              "\n",
              "Constructing DataFrame from dataclass:\n",
              "\n",
              ">>> from dataclasses import make_dataclass\n",
              ">>> Point = make_dataclass("Point", [("x", int), ("y", int)])\n",
              ">>> pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\n",
              "   x  y\n",
              "0  0  0\n",
              "1  0  3\n",
              "2  2  3\n",
              "\n",
              "Constructing DataFrame from Series/DataFrame:\n",
              "\n",
              ">>> ser = pd.Series([1, 2, 3], index=["a", "b", "c"])\n",
              ">>> df = pd.DataFrame(data=ser, index=["a", "c"])\n",
              ">>> df\n",
              "   0\n",
              "a  1\n",
              "c  3\n",
              "\n",
              ">>> df1 = pd.DataFrame([1, 2, 3], index=["a", "b", "c"], columns=["x"])\n",
              ">>> df2 = pd.DataFrame(data=df1, index=["a", "c"])\n",
              ">>> df2\n",
              "   x\n",
              "a  1\n",
              "c  3
\n", " \n", "
" ] }, "metadata": {}, "execution_count": 196 } ], "source": [ "gdf= df[['open','low','high','close','vol','gain']].groupby('gain').mean()\n", "type(gdf)" ] }, { "cell_type": "code", "execution_count": 197, "metadata": { "id": "U3MooOf9UwQk", "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "outputId": "6eb92ad5-5157-4f1e-f4c8-8d333d15f465" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " open low high close vol\n", "gain \n", "large_gain 166.503623 165.626087 169.394638 171.154348 3.481156e+07\n", "medium_gain 173.778021 171.991458 173.742292 175.615833 2.371978e+07\n", "small_gain 172.886860 169.252385 170.718140 173.350930 2.567338e+07" ], "text/html": [ "\n", "
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openlowhighclosevol
gain
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medium_gain173.778021171.991458173.742292175.6158332.371978e+07
small_gain172.886860169.252385170.718140173.3509302.567338e+07
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "gdf", "summary": "{\n \"name\": \"gdf\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"gain\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"large_gain\",\n \"medium_gain\",\n \"small_gain\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.967718614628015,\n \"min\": 166.5036231884058,\n \"max\": 173.77802083333333,\n \"num_unique_values\": 3,\n \"samples\": [\n 166.5036231884058,\n 173.77802083333333,\n 172.88686046511629\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.1929743913763877,\n \"min\": 165.62608695652176,\n \"max\": 171.99145833333333,\n \"num_unique_values\": 3,\n \"samples\": [\n 165.62608695652176,\n 171.99145833333333,\n 169.2523848837209\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"high\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.2285738507947133,\n \"min\": 169.39463768115942,\n \"max\": 173.74229166666666,\n \"num_unique_values\": 3,\n \"samples\": [\n 169.39463768115942,\n 173.74229166666666,\n 170.71813953488373\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.230829937328592,\n \"min\": 171.15434782608696,\n \"max\": 175.6158333333333,\n \"num_unique_values\": 3,\n \"samples\": [\n 171.15434782608696,\n 175.6158333333333,\n 173.35093023255814\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vol\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5921017.130611317,\n \"min\": 23719777.635416668,\n \"max\": 34811562.942028984,\n \"num_unique_values\": 3,\n \"samples\": [\n 34811562.942028984,\n 23719777.635416668,\n 25673379.151162792\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 197 } ], "source": [ "gdf" ] }, { "cell_type": "markdown", "metadata": { "id": "jN_z0OHDUwQk" }, "source": [ "We can also apply a customized aggregation function using the agg method, and the lambda functions. This is useful also for processing string attributes. For example, if we would like the aggregation function to be the max-min we can do the following" ] }, { "cell_type": "markdown", "metadata": { "id": "FxDlWIKnUwQq" }, "source": [ "If you want to remove the (hierarchical) index and have the group-by atrribute(s) to be part of the table, you can use the **reset_index** method. The result of this method is to make the index attribute one more column in the dataframe, and use the default index" ] }, { "cell_type": "code", "execution_count": 198, "metadata": { "id": "lomWA4iwUwQr", "colab": { "base_uri": "https://localhost:8080/", "height": 143 }, "outputId": "68d1971b-957b-4637-a577-f54f99393922" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " gain open low high close vol\n", "0 large_gain 166.503623 165.626087 169.394638 171.154348 3.481156e+07\n", "1 medium_gain 173.778021 171.991458 173.742292 175.615833 2.371978e+07\n", "2 small_gain 172.886860 169.252385 170.718140 173.350930 2.567338e+07" ], "text/html": [ "\n", "
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gainopenlowhighclosevol
0large_gain166.503623165.626087169.394638171.1543483.481156e+07
1medium_gain173.778021171.991458173.742292175.6158332.371978e+07
2small_gain172.886860169.252385170.718140173.3509302.567338e+07
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "gdf", "summary": "{\n \"name\": \"gdf\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"gain\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"large_gain\",\n \"medium_gain\",\n \"small_gain\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.967718614628015,\n \"min\": 166.5036231884058,\n \"max\": 173.77802083333333,\n \"num_unique_values\": 3,\n \"samples\": [\n 166.5036231884058,\n 173.77802083333333,\n 172.88686046511629\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 3.1929743913763877,\n \"min\": 165.62608695652176,\n \"max\": 171.99145833333333,\n \"num_unique_values\": 3,\n \"samples\": [\n 165.62608695652176,\n 171.99145833333333,\n 169.2523848837209\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"high\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.2285738507947133,\n \"min\": 169.39463768115942,\n \"max\": 173.74229166666666,\n \"num_unique_values\": 3,\n \"samples\": [\n 169.39463768115942,\n 173.74229166666666,\n 170.71813953488373\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2.230829937328592,\n \"min\": 171.15434782608696,\n \"max\": 175.6158333333333,\n \"num_unique_values\": 3,\n \"samples\": [\n 171.15434782608696,\n 175.6158333333333,\n 173.35093023255814\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"vol\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5921017.130611317,\n \"min\": 23719777.635416668,\n \"max\": 34811562.942028984,\n \"num_unique_values\": 3,\n \"samples\": [\n 34811562.942028984,\n 23719777.635416668,\n 25673379.151162792\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 198 } ], "source": [ "#This can be used to remove the hiearchical index, if necessary\n", "gdf = gdf.reset_index()\n", "gdf" ] }, { "cell_type": "code", "execution_count": 199, "metadata": { "id": "WkI9gxaDUwQr", "colab": { "base_uri": "https://localhost:8080/", "height": 175 }, "outputId": "12fc9613-222e-4c4e-abf7-ce74e19335bf" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " open low high close vol\n", "gain \n", "large_gain 166.503623 165.626087 169.394638 171.154348 3.481156e+07\n", "medium_gain 173.778021 171.991458 173.742292 175.615833 2.371978e+07\n", "small_gain 172.886860 169.252385 170.718140 173.350930 2.567338e+07" ], "text/html": [ "\n", "
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openlowhighclosevol
gain
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small_gain172.886860169.252385170.718140173.3509302.567338e+07
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "gtest", "summary": "{\n \"name\": \"gtest\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"B\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"b\",\n \"a\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"C\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"b\",\n \"a\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"A\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.5,\n \"min\": 2.0,\n \"max\": 3.0,\n \"num_unique_values\": 3,\n \"samples\": [\n 2.5,\n 2.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 204 } ], "source": [ "gtest = gtest.reset_index()\n", "gtest\n", "#the hierarchical index is flattened out" ] }, { "cell_type": "markdown", "metadata": { "id": "iAxIAy-MUwQt" }, "source": [ "## Joins\n", "\n", "We can join data frames in a similar way that we can do joins in SQL" ] }, { "cell_type": "code", "source": [ "client = TiingoClient({'api_key':'614c1590a592cc6696f6082f83b2666cd83882ef'})\n", "start = datetime(2018,1,1)\n", "end = datetime(2018,12,31)\n", "\n", "dfb = client.get_dataframe('META',frequency='daily',startDate=start,endDate=end)\n", "dfb = dfb[['open','close','low','high','volume']]\n", "\n", "dgoog = client.get_dataframe('GOOGL',frequency='daily',startDate=start,endDate=end)\n", "dgoog = dgoog[['open','close','low','high','volume']]\n", "\n", "print(dfb.head())\n", "print(dgoog.head())" ], "metadata": { "id": "74qmO8hjNVKL", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "7d7e3920-3681-4ac3-e9a7-dced8493d401" }, "execution_count": 205, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " open close low high volume\n", "date \n", "2018-01-02 00:00:00+00:00 177.68 181.42 177.55 181.58 17694891\n", "2018-01-03 00:00:00+00:00 181.88 184.67 181.33 184.78 16595495\n", "2018-01-04 00:00:00+00:00 184.90 184.33 184.10 186.21 13554357\n", "2018-01-05 00:00:00+00:00 185.59 186.85 184.93 186.90 13042388\n", "2018-01-08 00:00:00+00:00 187.20 188.28 186.33 188.90 14719216\n", " open close low high volume\n", "date \n", "2018-01-02 00:00:00+00:00 1053.02 1073.21 1053.02 1075.98 1555809\n", "2018-01-03 00:00:00+00:00 1073.93 1091.52 1073.43 1096.10 1550593\n", "2018-01-04 00:00:00+00:00 1097.09 1095.76 1094.26 1104.08 1289293\n", "2018-01-05 00:00:00+00:00 1103.45 1110.29 1101.80 1113.58 1493389\n", "2018-01-08 00:00:00+00:00 1111.00 1114.21 1110.00 1119.16 1148958\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "inO7sR1VUwQu" }, "source": [ "Perform join on the date (the index value). Note the \\_x and \\_y in the column names to differentiate columns with the same name coming from the left (x) and the right (y) dataframes" ] }, { "cell_type": "code", "execution_count": 206, "metadata": { "id": "U_nWeTUXUwQv", "colab": { "base_uri": "https://localhost:8080/", "height": 455 }, "outputId": "a1b36ba3-1577-4cc8-f773-72607432ac86" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " open_x close_x low_x high_x volume_x open_y \\\n", "date \n", "2018-01-02 00:00:00+00:00 177.68 181.42 177.55 181.58 17694891 1053.02 \n", "2018-01-03 00:00:00+00:00 181.88 184.67 181.33 184.78 16595495 1073.93 \n", "2018-01-04 00:00:00+00:00 184.90 184.33 184.10 186.21 13554357 1097.09 \n", "2018-01-05 00:00:00+00:00 185.59 186.85 184.93 186.90 13042388 1103.45 \n", "2018-01-08 00:00:00+00:00 187.20 188.28 186.33 188.90 14719216 1111.00 \n", "... ... ... ... ... ... ... \n", "2018-12-24 00:00:00+00:00 123.10 124.06 123.02 129.74 22066002 984.32 \n", "2018-12-26 00:00:00+00:00 126.00 134.18 125.89 134.24 39723370 997.99 \n", "2018-12-27 00:00:00+00:00 132.44 134.52 129.67 134.99 31202509 1026.20 \n", "2018-12-28 00:00:00+00:00 135.34 133.20 132.20 135.92 22627569 1059.50 \n", "2018-12-31 00:00:00+00:00 134.45 131.09 129.95 134.64 24625308 1057.83 \n", "\n", " close_y low_y high_y volume_y \n", "date \n", "2018-01-02 00:00:00+00:00 1073.21 1053.02 1075.98 1555809 \n", "2018-01-03 00:00:00+00:00 1091.52 1073.43 1096.10 1550593 \n", "2018-01-04 00:00:00+00:00 1095.76 1094.26 1104.08 1289293 \n", "2018-01-05 00:00:00+00:00 1110.29 1101.80 1113.58 1493389 \n", "2018-01-08 00:00:00+00:00 1114.21 1110.00 1119.16 1148958 \n", "... ... ... ... ... \n", "2018-12-24 00:00:00+00:00 984.67 977.66 1012.12 1817955 \n", "2018-12-26 00:00:00+00:00 1047.85 992.65 1048.45 2315862 \n", "2018-12-27 00:00:00+00:00 1052.90 1007.00 1053.34 2299806 \n", "2018-12-28 00:00:00+00:00 1046.68 1042.00 1064.23 1718352 \n", "2018-12-31 00:00:00+00:00 1044.96 1033.04 1062.99 1655504 \n", "\n", "[251 rows x 10 columns]" ], "text/html": [ "\n", "
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\"num_unique_values\": 249,\n \"samples\": [\n 209.94,\n 187.84,\n 186.9\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"low_x\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 20.07440767348364,\n \"min\": 123.02,\n \"max\": 214.27,\n \"num_unique_values\": 247,\n \"samples\": [\n 176.4,\n 185.63,\n 160.88\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"high_x\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 19.424564323437952,\n \"min\": 129.74,\n \"max\": 218.62,\n \"num_unique_values\": 246,\n \"samples\": [\n 177.95,\n 187.89,\n 171.77\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"volume_x\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 19101434,\n \"min\": 8855144,\n \"max\": 169803668,\n \"num_unique_values\": 251,\n \"samples\": [\n 19101995,\n 10464528,\n 17921935\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n 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open_fbclose_fblow_fbhigh_fbvolume_fbopen_googclose_googlow_googhigh_googvolume_goog
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open_fbclose_fblow_fbhigh_fbvolume_fbgainprofit_fbopen_googclose_googlow_googhigh_googvolume_googprofit_goog
date
2018-01-02 00:00:00+00:00177.68181.42177.55181.5817694891large_gain3.741053.021073.211053.021075.98155580920.19
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2018-01-09 00:00:00+00:00188.70187.87187.10188.8012342722negative-0.831118.441112.791108.201118.441335995-5.65
2018-01-11 00:00:00+00:00188.40187.77187.38188.408855144negative-0.631112.311112.051106.481114.851102461-0.26
2018-01-16 00:00:00+00:00181.50178.39178.04181.7535027166negative-3.111140.311130.701126.661148.881783881-9.61
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2018-12-21 00:00:00+00:00133.39124.95123.42134.9056901491negative-8.441032.04991.25981.191037.675232490-40.79
2018-12-24 00:00:00+00:00123.10124.06123.02129.7422066002small_gain0.96984.32984.67977.661012.1218179550.35
2018-12-26 00:00:00+00:00126.00134.18125.89134.2439723370large_gain8.18997.991047.85992.651048.45231586249.86
2018-12-28 00:00:00+00:00135.34133.20132.20135.9222627569negative-2.141059.501046.681042.001064.231718352-12.82
2018-12-31 00:00:00+00:00134.45131.09129.95134.6424625308negative-3.361057.831044.961033.041062.991655504-12.87
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\"description\": \"\"\n }\n },\n {\n \"column\": \"profit_goog\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 21.522483427541054,\n \"min\": -61.809999999999945,\n \"max\": 50.450000000000045,\n \"num_unique_values\": 128,\n \"samples\": [\n -3.6400000000001,\n -3.619999999999891,\n 9.429999999999836\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 211 } ], "source": [ "common_gain_dates = pd.merge(dfb, dgoog, on=['date','gain'],suffixes=('_fb', '_goog'))\n", "common_gain_dates" ] }, { "cell_type": "code", "source": [ "len(common_gain_dates)" ], "metadata": { "id": "fDySaaEoKHgJ", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "78e423ee-1e9a-437e-e8ef-34df0c45d752" }, "execution_count": 212, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "128" ] }, "metadata": {}, "execution_count": 212 } ] }, { "cell_type": "markdown", "metadata": { "id": "KuHCS8GtUwQx" }, "source": [ "More join examples, including left outer join" ] }, { "cell_type": "code", "execution_count": 213, "metadata": { "id": "C29DXoA2UwQx", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "875a5ff7-a00b-439e-ad7f-9658f4064d8e" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " key lval\n", "0 foo 1\n", "1 foo 2\n", "2 boo 3\n", "\n", "\n", " key rval\n", "0 foo 4\n", "1 hoo 5\n", "\n", "\n" ] } ], "source": [ "left = pd.DataFrame({'key': ['foo', 'foo', 'boo'], 'lval': [1, 2,3]})\n", "print(left)\n", "print('\\n')\n", "right = pd.DataFrame({'key': ['foo', 'hoo'], 'rval': [4, 5]})\n", "print(right)\n", "print('\\n')" ] }, { "cell_type": "code", "source": [ "dfm = pd.merge(left, right, on='key')\n", "print(dfm)" ], "metadata": { "id": "HXqMzvNNK1zu", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "a4c84f27-03b1-4d00-9c72-602046761550" }, "execution_count": 214, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " key lval rval\n", "0 foo 1 4\n", "1 foo 2 4\n" ] } ] }, { "cell_type": "markdown", "metadata": { "id": "HCvSn3nLUwQx" }, "source": [ "Left outer join" ] }, { "cell_type": "code", "execution_count": 215, "metadata": { "id": "Beg6qdd3UwQy", "colab": { "base_uri": "https://localhost:8080/", "height": 247 }, "outputId": "c4952e26-3cac-4106-8e2b-6132d15c4991" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " key lval rval\n", "0 foo 1 4.0\n", "1 foo 2 4.0\n", "2 boo 3 NaN\n", "\n", "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " key lval rval\n", "0 foo 1 4.0\n", "1 foo 2 4.0\n", "2 boo 3 0.0" ], "text/html": [ "\n", "
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open_FBclose_FBlow_FBhigh_FBvolume_FBgain_FBprofit_FBopen_GOOGclose_GOOGlow_GOOGhigh_GOOGvolume_GOOGgain_GOOGprofit_GOOG
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2018-01-05 00:00:00+00:00185.59186.85184.93186.9013042388medium_gain1.261103.451110.291101.801113.581493389large_gain6.84
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2018-12-24 00:00:00+00:00123.10124.06123.02129.7422066002small_gain0.96984.32984.67977.661012.121817955small_gain0.35
2018-12-26 00:00:00+00:00126.00134.18125.89134.2439723370large_gain8.18997.991047.85992.651048.452315862large_gain49.86
2018-12-27 00:00:00+00:00132.44134.52129.67134.9931202509medium_gain2.081026.201052.901007.001053.342299806large_gain26.70
2018-12-28 00:00:00+00:00135.34133.20132.20135.9222627569negative-2.141059.501046.681042.001064.231718352negative-12.82
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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "summary": "{\n \"name\": \"left\",\n \"rows\": 3,\n \"fields\": [\n {\n \"column\": \"key\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"boo\",\n \"foo\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"lval\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 1,\n \"max\": 3,\n \"num_unique_values\": 3,\n \"samples\": [\n 1,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"rval\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.0,\n \"min\": 4.0,\n \"max\": 4.0,\n \"num_unique_values\": 1,\n \"samples\": [\n 4.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 217 } ], "source": [ "left.index=left.key\n", "left = left.drop('key',axis=1)\n", "right.index =right.key\n", "right = right.drop('key',axis=1)\n", "left.join(right)" ] } ], "metadata": { "anaconda-cloud": {}, "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" }, "colab": { "provenance": [] } }, "nbformat": 4, "nbformat_minor": 0 }