Data cleaning

import pandas as pd

df = pd.read_csv("e://data/CustomerList.csv")

print(df)

Output

   id  name    type     city
0  100  John     NaN   Boston
1  101   Bob  Online  Chicago
2  102   NaN  Retail      NaN
3  103     ?     NaN      NaN


# Checking for null values for all columns
df.isnull().any()

Output

id      False
name     True
type     True
city     True
dtype: bool

#Checking for null values for type column
print(df[df['type'].isnull()].head())

Output

id  name type    city
0  100  John  NaN  Boston
3  103     ?  NaN     NaN

val={'type':'Regular'}

dfnew = df.fillna(value=val)

print(dfnew)

Output

         id  name     type     city
0  100  John  Regular   Boston
1  101   Bob   Online  Chicago
2  102   NaN   Retail      NaN
3  103     ?  Regular      NaN

# Keep rows with minimum 3 non na values
df2=dfnew.dropna(thresh=3)

print(df2)

Output

id  name     type     city
0  100  John  Regular   Boston
1  101   Bob   Online  Chicago
3  103     ?  Regular      NaN

# Column to be considered for fillna
val={'city':'Not Available'}

# NAN in city column is replced by Not Available
df3=df2.fillna(value=val)

# ? is replace with Unknown
print(df3.replace({'name':'?'},'Unknown'))

Output

    id     name     type           city
0  100     John  Regular         Boston
1  101      Bob   Online        Chicago
3  103  Unknown  Regular  Not Available