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 default value is False.

D. mean() - returns the average from a set of values.

Example:-

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)

Output:-

2016	2017	2018	2019
q1	500	A	54500.0	True
q2	500	A	51000.0	False
q3	47000	A	NaN	NaN
q4	49000	D	NaN	NaN

print(df2.mean())

Output:-

2016    24250.0
2018    52750.0

Explanation:-

mean() calculates the average among the indexes i.e for each column.

print(df2.mean(skipna=False))

Output:-

2016    24250.0
2018        NaN
Explanation:-
With skipna=False the NaN values are also included in the calculation.

E. median() - returns the median value for the requested axis.
Example:-

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)

print(df2.median())


Output:-
2016    23750.0
2018    52750.0
Do it yourself:-
print(df2.mean(axis=1))
print(df2.mean(axis=1,skipna=False,numeric_only=True))
print(df2.median(axis=1))
print(df2.median(skipna=False))

print(df2.median(skipna=False,numeric_only=False))

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