Manipulating Dataframe

Contents

3.13. Manipulating Dataframe#

import pandas as pd
import numpy as np

df=pd.DataFrame({
'Name':['Sahil Choudhary','Sonia Choudhary','Sourav','Vishal'],
'Age':[10,20,30,40],
'Gender':['M','F','M','M'],
'City':['J','K','L','P'],
'Work':[True,False,False,True]
}
)
df

	Name	Age	Gender	City	Work
0	Sahil Choudhary	10	M	J	True
1	Sonia Choudhary	20	F	K	False
2	Sourav	30	M	L	False
3	Vishal	40	M	P	True

3.13.1. Dataframe Inspection#

df['Age'].mean()

25.0

df['Age'].sum()

df['Name'].unique()

array(['Sahil Choudhary', 'Sonia Choudhary', 'Sourav', 'Vishal'],
      dtype=object)

df['Name'].nunique()

df['Name'].value_counts()

Sahil Choudhary    1
Sonia Choudhary    1
Sourav             1
Vishal             1
Name: Name, dtype: int64

3.13.2. Applying Conditions on columns#

3.13.2.1. Condition on one column#

df[df['Age']>10]

	Name	Age	Gender	City	Work
1	Sonia Choudhary	20	F	K	False
2	Sourav	30	M	L	False
3	Vishal	40	M	P	True

# If you don't want entire dataframe but want only one column
df[df['Age']>10]['Name']

1    Sonia Choudhary
2             Sourav
3             Vishal
Name: Name, dtype: object

3.13.2.2. Name contains Choudhary#

df[df['Name'].str.contains('Choudhary')]

	Name	Age	Gender	City	Work
0	Sahil Choudhary	10	M	J	True
1	Sonia Choudhary	20	F	K	False

3.13.2.3. Condition on multiple columns#

df[(df['Age']>10) & (df['Gender']=='M')]  # Note: It won't work if you replace & by and

	Name	Age	Gender	City	Work
2	Sourav	30	M	L	False
3	Vishal	40	M	P	True

df[df['Age'].between(10,20)]

	Name	Age	Gender	City	Work
0	Sahil Choudhary	10	M	J	True
1	Sonia Choudhary	20	F	K	False