{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "

Table of Contents

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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Import" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modules" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:04:42.730197Z", "start_time": "2020-09-02T03:04:41.853481Z" } }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "\n", "#Science and Data\n", "import pandas as pd\n", "import numpy as np\n", "\n", "# Infrastructure\n", "from pathlib import Path\n", "import sys\n", "import os\n", "\n", "#Plotting Tools\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set up options" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:04:42.756948Z", "start_time": "2020-09-02T03:04:42.733820Z" } }, "outputs": [], "source": [ "# Matplotlib\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (20, 10)\n", "plt.rcParams['axes.spines.left'] = False\n", "plt.rcParams['axes.spines.right'] = False\n", "plt.rcParams['axes.spines.top'] = False\n", "plt.rcParams['axes.spines.bottom'] = False\n", "plt.rcParams['xtick.bottom'] = False\n", "plt.rcParams['xtick.labelbottom'] = True\n", "plt.rcParams['ytick.labelleft'] = True\n", "plt.rcParams.update({'font.size': 18})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set up paths" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:04:42.805278Z", "start_time": "2020-09-02T03:04:42.759192Z" } }, "outputs": [], "source": [ "PROJECT_ROOT = !git rev-parse --show-toplevel\n", "PROJECT_ROOT = Path(PROJECT_ROOT[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the examples shown in this article, I will be using a data set taken from the Kaggle website. It is designed for a machine learning classification task and contains information about medical appointments and a target variable which denotes whether or not the patient showed up to their appointment.\n", "\n", "It can be downloaded [here](https://www.kaggle.com/somrikbanerjee/predicting-show-up-no-show).\n", "\n", "In the code below I have imported the data and the libraries that I will be using throughout the article." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:04:43.062803Z", "start_time": "2020-09-02T03:04:42.807516Z" } }, "outputs": [ { "data": { "text/html": [ "
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PatientIdAppointmentIDGenderScheduledDayAppointmentDayAgeNeighbourhoodScholarshipHipertensionDiabetesAlcoholismHandcapSMS_receivedNo-show
02.987250e+135642903F2016-04-29T18:38:08Z2016-04-29T00:00:00Z62JARDIM DA PENHA010000No
15.589978e+145642503M2016-04-29T16:08:27Z2016-04-29T00:00:00Z56JARDIM DA PENHA000000No
24.262962e+125642549F2016-04-29T16:19:04Z2016-04-29T00:00:00Z62MATA DA PRAIA000000No
38.679512e+115642828F2016-04-29T17:29:31Z2016-04-29T00:00:00Z8PONTAL DE CAMBURI000000No
48.841186e+125642494F2016-04-29T16:07:23Z2016-04-29T00:00:00Z56JARDIM DA PENHA011000No
\n", "
" ], "text/plain": [ " PatientId AppointmentID Gender ScheduledDay \\\n", "0 2.987250e+13 5642903 F 2016-04-29T18:38:08Z \n", "1 5.589978e+14 5642503 M 2016-04-29T16:08:27Z \n", "2 4.262962e+12 5642549 F 2016-04-29T16:19:04Z \n", "3 8.679512e+11 5642828 F 2016-04-29T17:29:31Z \n", "4 8.841186e+12 5642494 F 2016-04-29T16:07:23Z \n", "\n", " AppointmentDay Age Neighbourhood Scholarship Hipertension \\\n", "0 2016-04-29T00:00:00Z 62 JARDIM DA PENHA 0 1 \n", "1 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 0 \n", "2 2016-04-29T00:00:00Z 62 MATA DA PRAIA 0 0 \n", "3 2016-04-29T00:00:00Z 8 PONTAL DE CAMBURI 0 0 \n", "4 2016-04-29T00:00:00Z 56 JARDIM DA PENHA 0 1 \n", "\n", " Diabetes Alcoholism Handcap SMS_received No-show \n", "0 0 0 0 0 No \n", "1 0 0 0 0 No \n", "2 0 0 0 0 No \n", "3 0 0 0 0 No \n", "4 1 0 0 0 No " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(str(PROJECT_ROOT / \"notebooks\" / \"gist.pandas.value_counts\" / \"data\" / \"raw.csv\"))\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic Counts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `value_counts()` function can be used in the following way to get a count of unique values for one column in the data set. The code below gives a count of each value in the `Gender` column." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:04:43.104372Z", "start_time": "2020-09-02T03:04:43.064633Z" } }, "outputs": [ { "data": { "text/plain": [ "F 71840\n", "M 38687\n", "Name: Gender, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Gender'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To sort values in ascending or descending order we can use the `sort` argument. In the code below I have added `sort=True` to sort the `Age` column in descending order." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:04:43.129659Z", "start_time": "2020-09-02T03:04:43.106574Z" } }, "outputs": [ { "data": { "text/plain": [ " 0 3539\n", " 1 2273\n", " 52 1746\n", " 49 1652\n", " 53 1651\n", " ... \n", " 115 5\n", " 100 4\n", " 102 2\n", " 99 1\n", "-1 1\n", "Name: Age, Length: 104, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Age'].value_counts(sort=True)" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2020-09-01T23:43:05.094621Z", "start_time": "2020-09-01T23:43:05.075235Z" } }, "source": [ "# Combine with `groupby()`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `value_counts` function can be combined with other Pandas functions for richer analysis techniques. One example is to combine with the `groupby()` function. In the below example I am counting values in the Gender column and applying `groupby()` to further understand the number of no-shows in each group." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:04:43.181095Z", "start_time": "2020-09-02T03:04:43.133152Z" } }, "outputs": [ { "data": { "text/plain": [ "Gender No-show\n", "F No 57246\n", " Yes 14594\n", "M No 30962\n", " Yes 7725\n", "Name: No-show, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['No-show'].groupby(data['Gender']).value_counts(sort=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# `normalize`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above example displaying the absolute values does not easily enable us to understand the differences between the two groups. A better solution would be to show the relative frequencies of the unique values in each group.\n", "\n", "We can add the normalize argument to `value_counts()` to display the values in this way." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:04:43.225279Z", "start_time": "2020-09-02T03:04:43.183585Z" } }, "outputs": [ { "data": { "text/plain": [ "Gender No-show\n", "F No 0.796854\n", " Yes 0.203146\n", "M No 0.800321\n", " Yes 0.199679\n", "Name: No-show, dtype: float64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['No-show'].groupby(data['Gender']).value_counts(normalize=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Binning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For columns where there are a large number of unique values the output of the `value_counts()` function is not always particularly useful. A good example of this would be the Age column which we displayed value counts for earlier in this post.\n", "\n", "Fortunately `value_counts()` has a bins argument. This parameter allows us to specificy the number of bins (or groups we want to split the data into) as an integer. In the example below I have added `bins=9` to split the Age counts into 5 groups. We now have a count of values in each of these bins." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:04:43.263244Z", "start_time": "2020-09-02T03:04:43.227462Z" } }, "outputs": [ { "data": { "text/plain": [ "(-1.117, 11.889] 19945\n", "(50.556, 63.444] 19690\n", "(37.667, 50.556] 18987\n", "(24.778, 37.667] 18849\n", "(11.889, 24.778] 17323\n", "(63.444, 76.333] 10912\n", "(76.333, 89.222] 4404\n", "(89.222, 102.111] 412\n", "(102.111, 115.0] 5\n", "Name: Age, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Age'].value_counts(bins=9)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once again showing absolute numbers is not particularly useful so let’s add the `normalize=True` argument as well. Now we have a useful piece of analysis." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:04:43.307450Z", "start_time": "2020-09-02T03:04:43.265583Z" } }, "outputs": [ { "data": { "text/plain": [ "(-1.117, 11.889] 0.180454\n", "(50.556, 63.444] 0.178147\n", "(37.667, 50.556] 0.171786\n", "(24.778, 37.667] 0.170538\n", "(11.889, 24.778] 0.156731\n", "(63.444, 76.333] 0.098727\n", "(76.333, 89.222] 0.039845\n", "(89.222, 102.111] 0.003728\n", "(102.111, 115.0] 0.000045\n", "Name: Age, dtype: float64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Age'].value_counts(bins=9, normalize=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also parse a list to be use as the bins intervals. For this case, we define" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:04:43.329257Z", "start_time": "2020-09-02T03:04:43.309753Z" } }, "outputs": [], "source": [ "bins=[-np.inf, 10, 20, 30, 40, 50, 60, 70, 80, np.inf]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:04:43.365509Z", "start_time": "2020-09-02T03:04:43.331697Z" } }, "outputs": [ { "data": { "text/plain": [ "(-inf, 10.0] 18750\n", "(10.0, 20.0] 13099\n", "(20.0, 30.0] 13783\n", "(30.0, 40.0] 15052\n", "(40.0, 50.0] 14420\n", "(50.0, 60.0] 15661\n", "(60.0, 70.0] 11189\n", "(70.0, 80.0] 5721\n", "(80.0, inf] 2852\n", "Name: Age, dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[\"Age\"].value_counts(bins=bins, sort=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that it produces the same output as using `pd.cut` " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:04:43.399466Z", "start_time": "2020-09-02T03:04:43.367942Z" } }, "outputs": [ { "data": { "text/plain": [ "(-inf, 10.0] 18750\n", "(10.0, 20.0] 13099\n", "(20.0, 30.0] 13783\n", "(30.0, 40.0] 15052\n", "(40.0, 50.0] 14420\n", "(50.0, 60.0] 15661\n", "(60.0, 70.0] 11189\n", "(70.0, 80.0] 5721\n", "(80.0, inf] 2852\n", "Name: Age, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.groupby(pd.cut(data[\"Age\"].values, bins=bins))[\"Age\"].count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Combine with `nlargest()`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are other columns in our data set which have a large number of unique values where binning is still not going to provide us with a useful piece of analysis. A good example of this would be the `Neighbourhood` column.\n", "\n", "If we simply run `value_counts()` against this we get an output that is not particularly insightful." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:04:43.446052Z", "start_time": "2020-09-02T03:04:43.402189Z" } }, "outputs": [ { "data": { "text/plain": [ "JARDIM CAMBURI 7717\n", "MARIA ORTIZ 5805\n", "RESISTÊNCIA 4431\n", "JARDIM DA PENHA 3877\n", "ITARARÉ 3514\n", " ... \n", "ILHA DO BOI 35\n", "ILHA DO FRADE 10\n", "AEROPORTO 8\n", "ILHAS OCEÂNICAS DE TRINDADE 2\n", "PARQUE INDUSTRIAL 1\n", "Name: Neighbourhood, Length: 81, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Neighbourhood'].value_counts(sort=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A better way to display this might be to view the top 10 neighbourhoods. We can do this by combining with another Pandas function called `nlargest()` as shown below." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:04:43.490126Z", "start_time": "2020-09-02T03:04:43.448233Z" } }, "outputs": [ { "data": { "text/plain": [ "JARDIM CAMBURI 7717\n", "MARIA ORTIZ 5805\n", "RESISTÊNCIA 4431\n", "JARDIM DA PENHA 3877\n", "ITARARÉ 3514\n", "CENTRO 3334\n", "TABUAZEIRO 3132\n", "SANTA MARTHA 3131\n", "JESUS DE NAZARETH 2853\n", "BONFIM 2773\n", "Name: Neighbourhood, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Neighbourhood'].value_counts(sort=True).nlargest(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also use `nsmallest()` to display the bottom 10 neighbourhoods which might also prove useful." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:05:08.108130Z", "start_time": "2020-09-02T03:05:08.059691Z" } }, "outputs": [ { "data": { "text/plain": [ "PARQUE INDUSTRIAL 1\n", "ILHAS OCEÂNICAS DE TRINDADE 2\n", "AEROPORTO 8\n", "ILHA DO FRADE 10\n", "ILHA DO BOI 35\n", "PONTAL DE CAMBURI 69\n", "MORADA DE CAMBURI 96\n", "NAZARETH 135\n", "SEGURANÇA DO LAR 145\n", "UNIVERSITÁRIO 152\n", "Name: Neighbourhood, dtype: int64" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Neighbourhood'].value_counts(sort=True).nsmallest(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another handy combination is the Pandas plotting functionality together with `value_counts()`. Having the ability to display the analyses we get from `value_counts()` as visualisations can make it far easier to view trends and patterns.\n", "\n", "We can display all of the above examples and more with most plot types available in the Pandas library. A full list of available options can be found [here](https://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.DataFrame.plot.html)\n", "\n", "Let’s look a few examples.\n", "\n", "We can use a bar plot to view the top 10 neighbourhoods." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:06:02.122776Z", "start_time": "2020-09-02T03:06:01.776969Z" }, "hide_input": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data['Neighbourhood'].value_counts(sort=True).nlargest(10).plot.bar()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can make a pie chart to better visualise the `Gender` column." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2020-09-02T03:06:24.777428Z", "start_time": "2020-09-02T03:06:24.615225Z" } }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data['Gender'].value_counts().plot.pie()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `value_counts()` function is often one of my first starting points for data analysis as it enables me to very quickly plot trends and derive insights from individual columns in a data set. This article has given a quick overview of various types of analyses you can use this for but this function has more uses beyond the scope of this post." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# References" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "https://medium.com/m/signin?operation=login&redirect=https%3A%2F%2Ftowardsdatascience.com%2Fvaluable-data-analysis-with-pandas-value-counts-d87bbdf42f79" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": {}, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }