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Function Application Continued Aggregation Groupby...

Aggregation(group by) -  A groupby operation involves some combination of splitting the object, applying a function, and combining the results.  This can be used to group large amounts of data and compute operations on these groups. Groupby function works when you have categorical data as we have in the weather data below. Here 'weather', 'Year'and 'State'are all categorical. Groupby operation involves one of the following operations. Splitting the Object Applying a function    Combining the results gp = df.groupby(['Weather','State']) for name,group in gp: print(name) print(group) Output:- 1. Splitting the object -  import pandas as pd weather_data = {'Weather': ['Rainy', 'Stormy', 'Sunny', 'Cloudy', 'Rainy', 'Sunny', 'Cloudy', 'Rainy', 'Stormy', 'Cloudy', 'Sunny', 'Sunny'], 'State': ['CG...
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Descriptive Statistics - count & sum

F. Count() - counts the non-NA entries for each row and column. Values None, Nat, NaN are considered as NA in pandas. Example:-  1. import pandas as pd df2 = pd.DataFrame({2016:{'q1':500,'q2':500,'q3':47000,'q4':49000},2017:{'q1':'A','q2':'A','q3':'A','q4':'D'},2018:{'q1':54500,'q2':51000},2019:{'q1':True,'q2':'False'}}) print(df2.count()) Output:- 2016    4 2017    4 2018    2 2019    2 2.  import pandas as pd df2 = pd.DataFrame({2016:{'q1':500,'q2':500,'q3':47000,'q4':49000},2017:{'q1':'A','q2':'A','q3':'A','q4':'D'},2018:{'q1':54500,'q2':51000},2019:{'q1':True,'q2':'False'}}) print(df2.count(numeric_only=True)) Output:- 2016 4 2018 2 G. Sum() - Returns the sum of the values for...
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Descriptive Statistics - min(), max()

A. min() - Find out the minimum and maximum out of a given set of data. Example:- import pandas as pd df = pd.DataFrame({2016:{'q1':34500,'q2':56000,'q3':47000,'q4':49000},2017:\{'q1':44900,'q2':46100,'q3':57000,'q4':59000},2018:{'q1':54500,'q2':51000}}) print(df) Output:- 2016 2017 2018 q1 34500 44900 54500.0 q2 56000 46100 51000.0 q3 47000 57000 NaN q4 49000 59000 NaN 1. print(df.min()) Output:- 2016 34500.0 2017 44900.0 2018 51000.0 Explanation: min() finds the minimum among the indexes for each column and axis 0 by default. 2. print(df.min(axis=1)) Output:- q1 34500.0 q2 46100.0 q3 47000.0 q4 49000.0 Explanation: min(axis=1) finds the minimum among the columns for each indexes. import pandas as pd df2 = pd.DataFrame({2016:{'q1':34500,'q2':56000,'q3':47000,'q4':49000},2017:{'q1':'A','q2':'B...
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