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Pandas - Part2 (Combining DataFrame)

Import NUMPY, PANDAS, Series & DataFrame

In [4]:
import numpy as np

import pandas as pd

from pandas import Series, DataFrame

Creating DataFrame 1

In [9]:
# Creating DataFrame1

# We put np.nan in nan as it is easier to write nan than np.nan 
nan=np.nan

#nan is a missing value in np.array([nan,2,5])

df1 = DataFrame ({ 'Category': (['Books', 'Computers', 'Home']), 'sales_Number': np.array([nan,3,5])})
df1
Out[9]:
Category sales_Number
0 Books NaN
1 Computers 3.0
2 Home 5.0

Creating DataFrame 2

In [8]:
# Creating DataFrame2

# We put np.nan in nan as it is easier to write nan than np.nan 
nan=np.nan

df2 = DataFrame ({ 'Category': (['Books', 'Computers', 'Home']), 'sales_Number': np.array([4,3,5])})

df2
Out[8]:
Category sales_Number
0 Books 4
1 Computers 3
2 Home 5

Combining two Dataframe:

df1.combine_first(df2)

In [10]:
df1.combine_first(df2)
Out[10]:
Category sales_Number
0 Books 4.0
1 Computers 3.0
2 Home 5.0

Result: After combining two DataFrame, sales_Number for Books is no longer emty and it has the value of 4.