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Descriptive statistics - mode(), mean() and median()

C. mode() - returns the value that appears most from a set of values. Example:- 1. import pandas as pd 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}}) df2.mode() Output:- 2016 2017 2018 0 500.0 A 51000.0 1 NaN NaN 54500.0 Explanation:- Since by default axis=0 so mode is calculated among rows(indexes) i.e for each column. 2. df2.mode(axis=1) 0 1 2 q1 500 A 54500.0 q2 500 A 51000.0 q3 47000 A NaN q4 49000 D NaN Explanation:- Since axis=1 so the mode is calculated among columns i.e for each row. 3.  df2.mode(numeric_only=True) Output:- 2016 2018 0 500.0 51000.0 1 NaN 54500.0 Explanation: With numeric_only=True only numeric values are included for mode calculation and String/Text values are not considered. By...
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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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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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