From d2b6f1cda2d3d8b498a8b8a7a516cb76a2b9562c Mon Sep 17 00:00:00 2001 From: Prabhu Ramachandran Date: Fri, 19 Jul 2019 00:57:23 +0530 Subject: Adding pandas related content. --- cbse/data/sslc_small.csv | 41 +++ cbse/pandas.ipyml | 876 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 917 insertions(+) create mode 100644 cbse/data/sslc_small.csv create mode 100644 cbse/pandas.ipyml diff --git a/cbse/data/sslc_small.csv b/cbse/data/sslc_small.csv new file mode 100644 index 0000000..79dad21 --- /dev/null +++ b/cbse/data/sslc_small.csv @@ -0,0 +1,41 @@ +region;roll_number;name;fl;sl;math;sci;ss;total;pass;withheld;extra +A;010001;T N;053;036;28;16;44;177;;; +A;010002;A R;058;037;42;35;40;212;P;; +A;010003;A M;072;056;71;55;70;324;P;; +A;010004;S A;087;064;83;58;65;357;P;; +A;010005;N A;059;045;50;35;48;237;P;; +A;010006;A S;044;037;25;18;37;161;;; +A;010007;G M;062;046;49;36;44;237;P;; +A;010008;J R;087;071;97;71;81;407;P;; +A;010009;J K;043;040;45;35;35;198;P;; +B;019495;KARTHIK K;081;050;80;53;69;333;P;; +B;019496;MARI SELVI V;046;036;38;35;44;199;P;; +B;019497;AROCKIA ARUL JESURAJ A;089;063;98;86;95;431;P;; +B;019498;MOHAMEDDHARICK S;069;036;35;36;51;227;P;; +B;019499;SATHISHKUMAR V;077;057;61;53;78;326;P;; +B;019500;MARIMUTHU P;089;071;92;78;88;418;P;; +B;019501;JEYANDHIRA T;080;057;66;37;61;301;P;; +B;019502;VAIRA MUTHU M;078;049;41;53;74;295;P;; +B;019503;SARAVANAKUMAR C;073;044;50;53;72;292;P;; +C;199976;SEKAR S;065;053;43;18;59;238;;; +C;027394;RANI K;090;088;100;83;95;456;P;; +C;027395;OMPRAKASH B;081;075;100;69;88;413;P;; +C;027396;MAREESWARI V;075;059;84;48;58;324;P;; +C;027397;MOHAMED SHAHUL HAMEED B;086;082;99;64;83;414;P;; +C;027398;RAJA PON SUGANTHI T;057;044;50;45;58;254;P;; +C;027399;RAJASINGAM P;086;090;96;82;97;451;P;; +C;027400;AZHAGURANI M;075;075;57;57;67;331;P;; +C;027401;MUTHU LAKSHMI B;061;040;38;40;35;214;P;; +C;027402;RANJITHA G;085;077;79;50;66;357;P;; +C;027403;REGINA R;076;070;89;51;67;353;P;; +C;027404;AMALA P;062;044;51;47;46;250;P;; +C;027405;SHEEBA P;089;085;99;83;95;451;P;; +D;034890;PRABAKARAN S;074;047;44;53;53;271;P;; +D;034891;SABURAL BARVIN J;066;043;78;35;60;282;P;; +D;034892;AJITHA M;072;041;47;51;50;261;P;; +D;034893;NAGALAKSHMI M;085;075;99;81;76;416;P;; +D;034894;NITHYA R;072;054;62;59;60;307;P;; +D;034895;SIVAKUMAR G;076;047;46;43;62;274;P;; +D;034896;GOMATHI K;077;055;86;49;55;322;P;; +D;034897;MEENAKSHI V;072;050;43;55;50;270;P;; +D;034898;MEENAKSHI M;080;066;76;67;64;353;P;; diff --git a/cbse/pandas.ipyml b/cbse/pandas.ipyml new file mode 100644 index 0000000..60b115e --- /dev/null +++ b/cbse/pandas.ipyml @@ -0,0 +1,876 @@ +cells: + +- markdown: | + Pandas + ====== + + - Provides a powerful `DataFrame` object. + - Makes it easy to deal with "Tabular" data. + - Very easy to read, process and visualize data. + - See http://pandas.pydata.org + + +- code: | + %matplotlib inline + import numpy as np + import pandas as pd + + id: 0 + +- code: | + x = np.linspace(0, 2*np.pi, 100) + sin = np.sin(x) + cos = np.cos(x) + + id: 1 + +- code: | + df = pd.DataFrame({'x': x, 'sin': sin, 'cos': cos, 'x-data':x}) + # OR + #df = pd.DataFrame(dict(x=x, sin=sin, cos=cos)) + + id: 2 + +- code: | + df.head() # or df.tail() + + id: 3 + +- code: | + df.tail() + + id: 4 + +- code: | + df.describe() + + id: 5 + +- code: | + df1 = df[10:13] + df1.head() + + id: 6 + +- code: | + df.x[::10] + + id: 7 + +- code: | + df1.describe() + + id: 8 + +- code: | + # df.x-data[:5] will not work!! + df['x-data'][:5] + + id: 9 + +- code: | + df['x'][:5] + + id: 10 + +- code: | + df.x[:10] + + id: 11 + +- code: | + df.columns + + id: 12 + +- code: | + len(df) + + id: 13 + +- code: | + df.index + + id: 14 + +- code: | + df1 = df.copy() + df1.head() + + id: 15 + +- markdown: | + Indexing + ========= + + - Give me a data frame, where all cosine values are >0. + + +- code: | + y = np.linspace(10, 11, 11) + y + + id: 16 + +- code: | + y> 10.5 + + id: 17 + +- code: | + cond = y > 10.5 + y[y > 10.7] + + id: 18 + +- code: | + + + id: 19 + +- code: | + + + id: 20 + +- code: | + + + id: 21 + +- code: | + condition = df.cos > 0.0 + print(len(condition)) + + id: 22 + +- code: | + df_positive_cos = df[condition] + df_positive_cos.describe() + + id: 23 + +- code: | + # Combining conditionals + cond1 = df.sin > 0.0 + df_all_positive = df[condition & cond1] + df_all_positive.describe() + + id: 24 + +- code: | + df_all_positive = df[(df.cos > 0.0) & (df.sin > 0)] + df_all_positive.describe() + + id: 25 + +- code: | + c = np.array([True, False, True, False]) + c1 = np.array([False, True, False, False]) + ~(c | c1) + + id: 26 + +- code: | + cond1 = df_positive_cos.sin > 0.0 + df_all_positive = df_positive_cos[cond1] + df_all_positive.describe() + + id: 27 + +- code: | + + + id: 28 + +- code: | + # This adds a new column sincos + df['sincos'] = df.sin*df.cos + len(df.sincos) + + id: 29 + +- code: | + df.describe() + + id: 30 + +- code: | + # Remove a column with del. + if 'x-data' in df: + del df['x-data'] + df.head() + + id: 31 + +- markdown: | + Plotting + ========= + + +- code: | + df.plot() + # or + #df.plot.line() + + id: 32 + +- markdown: | + Notice that everything is plotted w.r.t. the index! + Let us fix this! + + +- code: | + df.plot.line(x='x', y=['sin', 'cos']) + + id: 33 + +- code: | + # See what this does + + df[(df.sin > 0.0) ^ (df.cos < 0.0)].plot.line(x='x', marker='o') + + id: 34 + +- code: | + df.plot.hist(y='cos') + # or + #df.plot(y='cos', kind='hist') + + id: 35 + +- markdown: | + Input and output CSV and other file formats + -------------------------------------------- + + - `pd.read_csv()` + - `df.to_csv()` + - Can read/save to clip board. + - Directly read from URLs. + + +- code: | + df.to_csv('sincos.csv', index=False) + + id: 36 + +- code: | + df1 = pd.read_csv('sincos.csv') + df1.head() + + id: 37 + +- code: | + + + id: 38 + +- code: | + + + id: 39 + +- markdown: | + ### Conversion to LaTeX and HTML + + +- code: | + print(df[:5].to_latex()) + + id: 40 + +- code: | + print(df[:5].to_latex(index=False)) + + id: 41 + +- code: | + print(df[:5].to_html()) + + id: 42 + +- code: | + from IPython.display import HTML + HTML(df[:5].to_html()) + + id: 43 + +- markdown: | + Selecting from the clipboard + ============================= + + - Let us select data from wikipedia: + - https://en.wikipedia.org/wiki/List_of_countries_by_GDP_(nominal)_per_capita + + Select some data and then do this: + + +- code: | + df2 = pd.read_clipboard() + df2.columns = ['index', 'country', 'GDP'] + df2.head() + + id: 44 + +- code: | + url = 'http://www.aero.iitb.ac.in/~prabhu/tmp/sslc_small.csv' + df = pd.read_csv(url, sep=';') + + id: 45 + +- code: | + df.head() + #df.describe() + + id: 46 + +- code: | + df.fl.iloc[0] = np.nan + + id: 47 + +- code: | + df.head() + + id: 48 + +- code: | + df.describe() + + id: 49 + +- code: | + pd.read_csv? + + id: 50 + +- markdown: | + Exercise + -------- + + Look at the following: + + - https://data.gov.in/catalog/annual-and-seasonal-maximum-temperature-india + - https://data.gov.in/catalog/annual-and-seasonal-minimum-temperature-india + + + Download the csv file into a `datafile.csv` on your machine. + + +- code: | + df = pd.read_csv('datafile.csv') + df.head() + + id: 51 + +- code: | + df.plot.line(x='YEAR') + + id: 52 + +- code: | + + + id: 53 + +- markdown: | + Exercise + --------- + + + Consider a smaller file: + + - File is at: http://www.aero.iitb.ac.in/~prabhu/tmp/sslc_small.csv + + +- code: | + url = 'http://www.aero.iitb.ac.in/~prabhu/tmp/sslc_small.csv' + df = pd.read_csv(url) + df.head() # Produces only one strange column of data! + + id: 54 + +- markdown: | + Notice that this data is read incorrectly, this is because the separator is not a comma but a ';' so use this. + + +- code: | + df = pd.read_csv(url, sep=';') + df.head() + + id: 55 + +- code: | + df['region'].value_counts() + + id: 56 + +- code: | + df.plot.scatter(x='fl', y='math') + + id: 57 + +- markdown: | + There are more options to `pd.read_csv`, for example if `'AA'` is a value indicating a non-existing value you can pass an option, called `na_values`. Read more on the documentation for `read_csv`. + + +- code: | + url = 'http://www.aero.iitb.ac.in/~prabhu/tmp/sslc1.csv.gz' + + id: 58 + +- markdown: | + - This has a very large CSV file that is gzipped to save space. + - It can be loaded with the same method. + - You can download it and see the file. + + To unzip it if you want you can do + + ``` + $ gunzip sslc1.csv.gz + ``` + + The file has missing values in the form of 'AA' entries for absent students. + + +- code: | + df = pd.read_csv(url, sep=';', na_values='AA') + + id: 59 + +- markdown: | + If you have the `sslc1.csv` file locally you can do this: + + +- code: | + df = pd.read_csv('sslc1.csv', sep=';', na_values='AA') + df.head() + + id: 60 + +- code: | + df.describe() + + id: 61 + +- code: | + df['pass'].value_counts() + + id: 62 + +- code: | + df.groupby('region')['pass'].value_counts() + + id: 63 + +- code: | + df.plot.hist(y='sl') + + id: 64 + +- markdown: | + ## Pivoting + + - Powerful operation to group the data + - Performs a multi-dimensional summarization of the data + + Here is a simple example + + +- code: | + pd.pivot_table(df, index=['region']) + + id: 65 + +- markdown: | + The default aggregation function here is an average, i.e. `np.average`. + + +- code: | + # This is not useful but tells you how this can be changed. + pd.pivot_table(df, index=['region'], aggfunc=np.sum) + + id: 66 + +- markdown: | + More information + ================== + + - http://pandas.pydata.org + - Go through the tutorials here: + + http://nbviewer.jupyter.org/github/jvns/pandas-cookbook/tree/v0.1/cookbook/ + + - Go over chapter 1 to 7. + + Excellent material on pivot tables with pandas + + - https://pbpython.com/pandas-pivot-table-explained.html + + An excellent book on data science related tools has a nice section on pivot tables. + + - https://jakevdp.github.io/PythonDataScienceHandbook/03.09-pivot-tables.html + + Also has other material on pandas + + - https://jakevdp.github.io/PythonDataScienceHandbook/03.00-introduction-to-pandas.html + + The notebooks can also be edited live if you wish. + + +- code: | + + + id: 67 + +# The lines below here may be deleted if you do not need them. +# --------------------------------------------------------------------------- +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: 3.6.0 +nbformat: 4 +nbformat_minor: 1 + +# --------------------------------------------------------------------------- +data: + [{execution_count: 1, outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, + outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, + {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, + outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, + {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, + outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, + {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, + outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, + {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, + outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, + {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, + outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, + {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, + outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, + {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, + outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, + {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, + outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, + {execution_count: 56, outputs: []}, {execution_count: 62, outputs: [{data: {text/html: "
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regionroll_numbernameflslmathscisstotalpasswithheldextra
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", text/plain: ' region roll_number name fl sl math sci ss total + pass withheld extra + + 0 A 10001 T N 53 36 28 16 44 177 NaN NaN NaN + + 1 A 10002 A R 58 37 42 35 40 212 P NaN NaN + + 2 A 10003 A M 72 56 71 55 70 324 P NaN NaN + + 3 A 10004 S A 87 64 83 58 65 357 P NaN NaN + + 4 A 10005 N A 59 45 50 35 48 237 P NaN NaN'}, + execution_count: 62, metadata: {}, output_type: execute_result}]}, {execution_count: 66, + outputs: []}, {execution_count: 68, outputs: [{data: {text/html: "
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regionroll_numbernameflslmathscisstotalpasswithheldextra
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2A10003A M72.056715570324PNaNNaN
3A10004S A87.064835865357PNaNNaN
4A10005N A59.045503548237PNaNNaN
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", text/plain: " region roll_number name fl sl math sci\ + \ ss total pass withheld \\\n0 A 10001 T N NaN 36\ + \ 28 16 44 177 NaN NaN \n1 A 10002 A R\ + \ 58.0 37 42 35 40 212 P NaN \n2 A 10003\ + \ A M 72.0 56 71 55 70 324 P NaN \n3 A \ + \ 10004 S A 87.0 64 83 58 65 357 P NaN \n4\ + \ A 10005 N A 59.0 45 50 35 48 237 P \ + \ NaN \n\n extra \n0 NaN \n1 NaN \n2 NaN \n3 NaN \ + \ \n4 NaN "}, execution_count: 68, metadata: {}, output_type: execute_result}]}, + {execution_count: 67, outputs: [{data: {text/html: "
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roll_numberflslmathscisstotalwithheldextra
count40.00000039.00000040.00000040.00000040.00000040.00000040.0000000.00.0
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std29338.52309712.61674215.90305223.91532918.09219617.55869381.745869NaNNaN
min10001.00000043.00000036.00000025.00000016.00000035.000000161.000000NaNNaN
25%19495.75000065.50000044.00000044.75000036.75000050.000000247.000000NaNNaN
50%27395.50000075.00000053.50000061.50000052.00000061.500000304.000000NaNNaN
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", text/plain: " roll_number fl sl \ + \ math sci ss \\\ncount 40.000000 39.000000 40.000000\ + \ 40.000000 40.000000 40.000000 \nmean 27708.800000 73.230769\ + \ 56.375000 65.425000 51.825000 63.500000 \nstd 29338.523097\ + \ 12.616742 15.903052 23.915329 18.092196 17.558693 \nmin \ + \ 10001.000000 43.000000 36.000000 25.000000 16.000000 35.000000\ + \ \n25% 19495.750000 65.500000 44.000000 44.750000 36.750000\ + \ 50.000000 \n50% 27395.500000 75.000000 53.500000 61.500000\ + \ 52.000000 61.500000 \n75% 29276.250000 83.000000 70.250000\ + \ 86.750000 60.250000 74.500000 \nmax 199976.000000 90.000000\ + \ 90.000000 100.000000 86.000000 97.000000 \n\n total\ + \ withheld extra \ncount 40.000000 0.0 0.0 \nmean 309.850000\ + \ NaN NaN \nstd 81.745869 NaN NaN \nmin 161.000000\ + \ NaN NaN \n25% 247.000000 NaN NaN \n50% 304.000000\ + \ NaN NaN \n75% 357.000000 NaN NaN \nmax 456.000000\ + \ NaN NaN "}, execution_count: 67, metadata: {}, output_type: execute_result}]}, + {execution_count: 69, outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, + outputs: []}, {execution_count: null, outputs: []}, {execution_count: 70, 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 \n
region;roll_number;name;fl;sl;math;sci;ss;total;pass;withheld;extra
0A;010001;T\ + \ N;053;036;28;16;44;177;;;
1A;010002;A R;058;037;42;35;40;212;P;;
2A;010003;A M;072;056;71;55;70;324;P;;
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", text/plain: " region;roll_number;name;fl;sl;math;sci;ss;total;pass;withheld;extra\n\ + 0 A;010001;T N;053;036;28;16;44;177;;; \n\ + 1 A;010002;A R;058;037;42;35;40;212;P;; \n\ + 2 A;010003;A M;072;056;71;55;70;324;P;; \n\ + 3 A;010004;S A;087;064;83;58;65;357;P;; \n\ + 4 A;010005;N A;059;045;50;35;48;237;P;; "}, + execution_count: 70, metadata: {}, output_type: execute_result}]}, {execution_count: 71, + outputs: [{data: {text/html: "
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regionroll_numbernameflslmathscisstotalpasswithheldextra
0A10001T N5336281644177NaNNaNNaN
1A10002A R5837423540212PNaNNaN
2A10003A M7256715570324PNaNNaN
3A10004S A8764835865357PNaNNaN
4A10005N A5945503548237PNaNNaN
\n\ +
", text/plain: ' region roll_number name fl sl math sci ss total + pass withheld extra + + 0 A 10001 T N 53 36 28 16 44 177 NaN NaN NaN + + 1 A 10002 A R 58 37 42 35 40 212 P NaN NaN + + 2 A 10003 A M 72 56 71 55 70 324 P NaN NaN + + 3 A 10004 S A 87 64 83 58 65 357 P NaN NaN + + 4 A 10005 N A 59 45 50 35 48 237 P NaN NaN'}, + execution_count: 71, metadata: {}, output_type: execute_result}]}, {execution_count: 72, + outputs: [{data: {text/plain: 'C 13 + + B 9 + + A 9 + + D 9 + + Name: region, dtype: int64'}, execution_count: 72, metadata: {}, output_type: execute_result}]}, + {execution_count: 73, outputs: [{data: {text/plain: }, execution_count: 73, metadata: {}, output_type: execute_result}, + {data: {image/png: 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}, metadata: {needs_background: light}, + output_type: display_data}]}, {execution_count: 3, outputs: []}, {execution_count: 4, + outputs: []}, {execution_count: null, outputs: []}, {execution_count: null, outputs: []}, + {execution_count: 14, outputs: [{data: {text/plain: 'P 159072 + + Name: pass, dtype: int64'}, execution_count: 14, metadata: {}, output_type: execute_result}]}, + {execution_count: 18, outputs: [{data: {text/plain: 'region pass + + A P 31013 + + B P 36202 + + C P 26681 + + D P 22080 + + E P 20880 + + F P 22216 + + Name: pass, dtype: int64'}, execution_count: 18, metadata: {}, output_type: execute_result}]}, + {execution_count: null, outputs: []}, {execution_count: 7, outputs: [{data: {text/html: "
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flmathroll_numberscislsstotal
region
A54.15649763.149551105609.08167450.43275673.68637762.453164303.789099
B55.23983663.638822105746.14228052.74765374.45509364.448891310.482572
C54.08034662.733572124099.11787950.34422274.07671664.647672305.822393
D52.96829662.58999386303.81130751.18667073.66542364.119590304.428818
E52.08538962.72415397040.59568248.74378272.77239261.520147297.731881
F53.39622961.694766105989.11694649.73571272.04142561.167210297.852010
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", + text/plain: " fl math roll_number sci \ + \ sl ss \\\nregion \ + \ \nA 54.156497 63.149551 105609.081674\ + \ 50.432756 73.686377 62.453164 \nB 55.239836 63.638822 105746.142280\ + \ 52.747653 74.455093 64.448891 \nC 54.080346 62.733572 124099.117879\ + \ 50.344222 74.076716 64.647672 \nD 52.968296 62.589993 \ + \ 86303.811307 51.186670 73.665423 64.119590 \nE 52.085389\ + \ 62.724153 97040.595682 48.743782 72.772392 61.520147 \nF \ + \ 53.396229 61.694766 105989.116946 49.735712 72.041425 61.167210\ + \ \n\n total \nregion \nA 303.789099\ + \ \nB 310.482572 \nC 305.822393 \nD 304.428818 \n\ + E 297.731881 \nF 297.852010 "}, execution_count: 7, metadata: {}, + output_type: execute_result}]}, {execution_count: 8, outputs: [{data: {text/html: "
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B0.02282731.02630638.043716512682180377.03078048.02663866.012835660.0
C0.01689470.01960926.038794625241573559.02315490.02020563.09560008.0
D0.01348255.01593729.021981580741303315.01875890.01632613.07753802.0
E0.01290103.01553740.024046659611207286.01802936.01523731.07377796.0
F0.01407578.01629112.027995965351313172.01902542.01615059.07867463.0
\n
", text/plain: " extra\ + \ fl math roll_number sci sl \\\nregion\ + \ \n\ + A 0.0 1970430.0 2297949.0 3844276182 1835248.0 2681742.0\ + \ \nB 0.0 2282731.0 2630638.0 4371651268 2180377.0 3078048.0\ + \ \nC 0.0 1689470.0 1960926.0 3879462524 1573559.0 2315490.0\ + \ \nD 0.0 1348255.0 1593729.0 2198158074 1303315.0 1875890.0\ + \ \nE 0.0 1290103.0 1553740.0 2404665961 1207286.0 1802936.0\ + \ \nF 0.0 1407578.0 1629112.0 2799596535 1313172.0 1902542.0\ + \ \n\n ss total \nregion \ + \ \nA 2272858.0 11058227.0 \nB 2663866.0 12835660.0 \ + \ \nC 2020563.0 9560008.0 \nD 1632613.0 7753802.0 \n\ + E 1523731.0 7377796.0 \nF 1615059.0 7867463.0 "}, execution_count: 8, + metadata: {}, output_type: execute_result}]}, {execution_count: null, outputs: []}] + -- cgit