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

\n", " \n", " \"Skills\n", " \n", "

\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Descriptive Statistics\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Estimated time needed: **30** minutes\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this lab, you'll go over some hands-on exercises using Python.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Objectives\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Import Libraries\n", "* Read in Data\n", "* Lab exercises and questions\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import Libraries\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All Libraries required for this lab are listed below. The libraries pre-installed on Skills Network Labs are commented. If you run this notebook in a different environment, e.g. your desktop, you may need to uncomment and install certain libraries.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#! mamba install pandas==1.3.3\n", "#! mamba install numpy=1.21.2\n", "#! mamba install matplotlib=3.4.3-y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the libraries we need for the lab\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as pyplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read in the csv file from the URL using the request library\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "ratings_url = 'https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ST0151EN-SkillsNetwork/labs/teachingratings.csv'\n", "ratings_df=pd.read_csv(ratings_url)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Description\n", "\n", "| Variable | Description |\n", "| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |\n", "| minority | Does the instructor belong to a minority (non-Caucasian) group? |\n", "| age | The professor's age |\n", "| gender | Indicating whether the instructor was male or female. |\n", "| credits | Is the course a single-credit elective? |\n", "| beauty | Rating of the instructor's physical appearance by a panel of six students averaged across the six panelists and standardized to have a mean of zero. |\n", "| eval | Course overall teaching evaluation score, on a scale of 1 (very unsatisfactory) to 5 (excellent). |\n", "| division | Is the course an upper or lower division course? |\n", "| native | Is the instructor a native English speaker? |\n", "| tenure | Is the instructor on a tenure track? |\n", "| students | Number of students that participated in the evaluation. |\n", "| allstudents | Number of students enrolled in the course. |\n", "| prof | Indicating instructor identifier. |\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Display information about the dataset\n", "\n", "1. Structure of the dataframe\n", "2. Describe the dataset\n", "3. Number of rows and columns\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "print out the first five rows of the data\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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minorityagegendercreditsbeautyevaldivisionnativetenurestudentsallstudentsprofPrimaryLastvisminfemalesingle_creditupper_divisionEnglish_speakertenured_prof
0yes36femalemore0.2899164.3upperyesyes244310110111
1yes36femalemore0.2899163.7upperyesyes8612510110111
2yes36femalemore0.2899163.6upperyesyes7612510110111
3yes36femalemore0.2899164.4upperyesyes7712311110111
4no59malemore-0.7377324.5upperyesyes172020000111
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" ], "text/plain": [ " minority age gender credits beauty eval division native tenure \\\n", "0 yes 36 female more 0.289916 4.3 upper yes yes \n", "1 yes 36 female more 0.289916 3.7 upper yes yes \n", "2 yes 36 female more 0.289916 3.6 upper yes yes \n", "3 yes 36 female more 0.289916 4.4 upper yes yes \n", "4 no 59 male more -0.737732 4.5 upper yes yes \n", "\n", " students allstudents prof PrimaryLast vismin female single_credit \\\n", "0 24 43 1 0 1 1 0 \n", "1 86 125 1 0 1 1 0 \n", "2 76 125 1 0 1 1 0 \n", "3 77 123 1 1 1 1 0 \n", "4 17 20 2 0 0 0 0 \n", "\n", " upper_division English_speaker tenured_prof \n", "0 1 1 1 \n", "1 1 1 1 \n", "2 1 1 1 \n", "3 1 1 1 \n", "4 1 1 1 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratings_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "get information about each variable\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 463 entries, 0 to 462\n", "Data columns (total 19 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 minority 463 non-null object \n", " 1 age 463 non-null int64 \n", " 2 gender 463 non-null object \n", " 3 credits 463 non-null object \n", " 4 beauty 463 non-null float64\n", " 5 eval 463 non-null float64\n", " 6 division 463 non-null object \n", " 7 native 463 non-null object \n", " 8 tenure 463 non-null object \n", " 9 students 463 non-null int64 \n", " 10 allstudents 463 non-null int64 \n", " 11 prof 463 non-null int64 \n", " 12 PrimaryLast 463 non-null int64 \n", " 13 vismin 463 non-null int64 \n", " 14 female 463 non-null int64 \n", " 15 single_credit 463 non-null int64 \n", " 16 upper_division 463 non-null int64 \n", " 17 English_speaker 463 non-null int64 \n", " 18 tenured_prof 463 non-null int64 \n", "dtypes: float64(2), int64(11), object(6)\n", "memory usage: 68.9+ KB\n" ] } ], "source": [ "ratings_df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "get the number of rows and columns - prints as (number of rows, number of columns)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(463, 19)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratings_df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lab Exercises\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Can you identify whether the teachers' Rating data is a time series or cross-sectional?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print out the first ten rows of the data\n", "\n", "1. Does it have a date or time variable? - No - it is not a time series dataset\n", "2. Does it observe more than one teacher being rated? - Yes - it is cross-sectional dataset\n", "\n", "> The dataset is a Cross-sectional\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ratings_df.head(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Find the mean, median, minimum, and maximum values for students\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find Mean value for students\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "36.62419006479482" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratings_df['students'].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find the Median value for students\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "23.0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratings_df['students'].median()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find the Minimum value for students\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratings_df['students'].min()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find the Maximum value for students\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "380" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratings_df['students'].max()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Produce a descriptive statistics table\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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agebeautyevalstudentsallstudentsprofPrimaryLastvisminfemalesingle_creditupper_divisionEnglish_speakertenured_prof
count463.0000004.630000e+02463.000000463.000000463.000000463.000000463.000000463.000000463.000000463.000000463.000000463.000000463.000000
mean48.3650116.271140e-083.99827236.62419055.17710645.4341250.2030240.1382290.4211660.0583150.6609070.9395250.779698
std9.8027427.886477e-010.55486645.01848175.07280027.5089020.4026850.3455130.4942800.2345920.4739130.2386230.414899
min29.000000-1.450494e+002.1000005.0000008.0000001.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
25%42.000000-6.562689e-013.60000015.00000019.00000020.0000000.0000000.0000000.0000000.0000000.0000001.0000001.000000
50%48.000000-6.801430e-024.00000023.00000029.00000044.0000000.0000000.0000000.0000000.0000001.0000001.0000001.000000
75%57.0000005.456024e-014.40000040.00000060.00000070.5000000.0000000.0000001.0000000.0000001.0000001.0000001.000000
max73.0000001.970023e+005.000000380.000000581.00000094.0000001.0000001.0000001.0000001.0000001.0000001.0000001.000000
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" ], "text/plain": [ " age beauty eval students allstudents \\\n", "count 463.000000 4.630000e+02 463.000000 463.000000 463.000000 \n", "mean 48.365011 6.271140e-08 3.998272 36.624190 55.177106 \n", "std 9.802742 7.886477e-01 0.554866 45.018481 75.072800 \n", "min 29.000000 -1.450494e+00 2.100000 5.000000 8.000000 \n", "25% 42.000000 -6.562689e-01 3.600000 15.000000 19.000000 \n", "50% 48.000000 -6.801430e-02 4.000000 23.000000 29.000000 \n", "75% 57.000000 5.456024e-01 4.400000 40.000000 60.000000 \n", "max 73.000000 1.970023e+00 5.000000 380.000000 581.000000 \n", "\n", " prof PrimaryLast vismin female single_credit \\\n", "count 463.000000 463.000000 463.000000 463.000000 463.000000 \n", "mean 45.434125 0.203024 0.138229 0.421166 0.058315 \n", "std 27.508902 0.402685 0.345513 0.494280 0.234592 \n", "min 1.000000 0.000000 0.000000 0.000000 0.000000 \n", "25% 20.000000 0.000000 0.000000 0.000000 0.000000 \n", "50% 44.000000 0.000000 0.000000 0.000000 0.000000 \n", "75% 70.500000 0.000000 0.000000 1.000000 0.000000 \n", "max 94.000000 1.000000 1.000000 1.000000 1.000000 \n", "\n", " upper_division English_speaker tenured_prof \n", "count 463.000000 463.000000 463.000000 \n", "mean 0.660907 0.939525 0.779698 \n", "std 0.473913 0.238623 0.414899 \n", "min 0.000000 0.000000 0.000000 \n", "25% 0.000000 1.000000 1.000000 \n", "50% 1.000000 1.000000 1.000000 \n", "75% 1.000000 1.000000 1.000000 \n", "max 1.000000 1.000000 1.000000 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratings_df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create a histogram of the beauty variable and briefly comment on the distribution of data\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "using the matplotlib library, create a histogram\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([16., 51., 94., 66., 94., 42., 29., 40., 11., 20.]),\n", " array([-1.45049405, -1.10844234, -0.76639063, -0.42433892, -0.08228722,\n", " 0.25976449, 0.6018162 , 0.94386791, 1.28591962, 1.62797133,\n", " 1.97002304]),\n", " )" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pyplot.hist(ratings_df['beauty'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "here are few conclusions from the histogram\n", "most of the data for beauty is around the -0.5 and 0\n", "the distribution is skewed to the right\n", "therefore looking at the data we can say the mean is close to 0\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Does average beauty score differ by gender? Produce the means and standard deviations for both male and female instructors.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use a group by gender to view the mean scores of the beauty we can say that beauty scores differ by gender as the mean beauty score for women is higher than men\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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genderbeauty
meanstdvar
0female0.1161090.817810.668813
1male-0.0844820.757130.573246
\n", "
" ], "text/plain": [ " gender beauty \n", " mean std var\n", "0 female 0.116109 0.81781 0.668813\n", "1 male -0.084482 0.75713 0.573246" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ratings_df.groupby('gender').agg({'beauty':['mean', 'std', 'var']}).reset_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate the percentage of males and females that are tenured professors. Will you say that tenure status differ by gender?\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First groupby to get the total sum\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "tenure_count = ratings_df[ratings_df.tenure == 'yes'].groupby('gender').agg({'tenure': 'count'}).reset_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find the percentage\n" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
gendertenurepercentage
0female14540.166205
1male21659.833795
\n", "
" ], "text/plain": [ " gender tenure percentage\n", "0 female 145 40.166205\n", "1 male 216 59.833795" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tenure_count['percentage'] = 100 * tenure_count.tenure/tenure_count.tenure.sum()\n", "tenure_count" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Practice Questions\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 1: Calculate the percentage of visible minorities are tenure professors. Will you say that tenure status differed if teacher was a visible minority?\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
minoritytenurepercentage
0no30785.041551
1yes5414.958449
\n", "
" ], "text/plain": [ " minority tenure percentage\n", "0 no 307 85.041551\n", "1 yes 54 14.958449" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## insert code here\n", "## first groupby to get the total sum\n", "tenure_count = ratings_df[ratings_df.tenure == 'yes'].groupby('minority').agg({'tenure': 'count'}).reset_index()\n", "# Find the percentage\n", "tenure_count['percentage'] = 100 * tenure_count.tenure/tenure_count.tenure.sum()\n", "##print to see\n", "tenure_count" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Double-click **here** for the solution.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 2: Does average age differ by tenure? Produce the means and standard deviations for both tenured and untenured professors.\n" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
tenureage
meanstd
0no50.1862756.946372
1yes47.85041610.420056
\n", "
" ], "text/plain": [ " tenure age \n", " mean std\n", "0 no 50.186275 6.946372\n", "1 yes 47.850416 10.420056" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## insert code here\n", "ratings_df.groupby('tenure').agg({'age':['mean', 'std']}).reset_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Double-click **here** for the solution.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 3: Create a histogram for the age variable.\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([41., 37., 58., 48., 75., 74., 53., 69., 0., 8.]),\n", " array([29. , 33.4, 37.8, 42.2, 46.6, 51. , 55.4, 59.8, 64.2, 68.6, 73. ]),\n", " )" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "## insert code here\n", "pyplot.hist(ratings_df['age'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Double-click **here** for the solution.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 4: Create a bar plot for the gender variable.\n" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Gender distribution bar plot')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "## insert code here\n", "pyplot.bar(ratings_df.gender.unique(), ratings_df.gender.value_counts())\n", "pyplot.xlabel('Gender')\n", "pyplot.ylabel('Count')\n", "pyplot.title('Gender distribution bar plot')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Double-click **here** for the solution.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Note:Bar plot can be rendered vertically or horizontally. Try to replace **pyplot.bar** with **pyplot.barh** in the above cell and see the difference.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Question 5: What is the Median evaluation score for tenured Professors?\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "4.0" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## insert code here\n", "ratings_df[ratings_df['tenure'] == 'yes']['eval'].median()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Double-click **here** for the solution.\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Authors\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Aije Egwaikhide](https://www.linkedin.com/in/aije-egwaikhide/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkST0151ENSkillsNetwork20531532-2022-01-01) is a Data Scientist at IBM who holds a degree in Economics and Statistics from the University of Manitoba and a Post-grad in Business Analytics from St. Lawrence College, Kingston. She is a current employee of IBM where she started as a Junior Data Scientist at the Global Business Services (GBS) in 2018. Her main role was making meaning out of data for their Oil and Gas clients through basic statistics and advanced Machine Learning algorithms. The highlight of her time in GBS was creating a customized end-to-end Machine learning and Statistics solution on optimizing operations in the Oil and Gas wells. She moved to the Cognitive Systems Group as a Senior Data Scientist where she will be providing the team with actionable insights using Data Science techniques and further improve processes through building machine learning solutions. She recently joined the IBM Developer Skills Network group where she brings her real-world experience to the courses she creates.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Change Log\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n", "| ----------------- | ------- | --------------- | -------------------------------------- |\n", "| 2020-08-14 | 0.1 | Aije Egwaikhide | Created the initial version of the lab |\n", "| 2022-05-10 | 0.2 | Lakshmi Holla | Added exercise for Bar plot |\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright © 2020 IBM Corporation. This notebook and its source code are released under the terms of the [MIT License](https://cognitiveclass.ai/mit-license/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkST0151ENSkillsNetwork20531532-2022-01-01).\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python", "language": "python", "name": "conda-env-python-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.12" } }, "nbformat": 4, "nbformat_minor": 4 }