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 the requested axis.

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.sum())

Output:-

2016     97000
2017      AAAD

2018    105500
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.sum(axis=1))

Output:-

q1    55000.0
q2    51500.0
q3    47000.0
q4    49000.0