Concatenation of two or more data frames in pandas can be done using concat() function of pandas package , which allows you to concatenate two or more DataFrames . This can be done in both ways i.e row wise and column wise .
Concatenating DataFrames along row
The below code is for concatenating two dataframes row wise
Program :
import pandas as pd
df1 = pd.DataFrame({ #creating a dataframe
'A': [1, 2],
'B': [3, 4]
})
df2 = pd.DataFrame({ #creating another dataframe
'A': [5, 6],
'B': [7, 8]
})
result = pd.concat([df1, df2], axis=0) # Concatenate along rows (axis=0)
print(result)
Output:
A B 0 1 3 1 2 4 0 5 7 1 6 8
Here, we can understand that the two dataframes are concatenated row wise . This is by specifing axis = 0 , by default axis is always zero.
This means that the rows of the second dataframe will be appended to the rows of the first dataframe . The columns in both DataFrames should ideally match in order for the data to align correctly. If the columns do not match, pandas will automatically fill in missing values with NaN.
Let’s understand this by a simple code
Code:
import pandas as pd
df1 = pd.DataFrame({ #creating a dataframe
'A': [1, 2],
'B': [3, 4],
'C': [5, 6]
})
df2 = pd.DataFrame({ #creating another dataframe
'A': [7, 8],
'B': [9, 10]
})
result = pd.concat([df1, df2], axis=0) # Concatenate along rows (axis=0)
print(result)
Output:
A B C 0 1 3 5.0 1 2 4 6.0 0 7 9 NaN 1 8 10 NaN
Concatenating DataFrames along Column
The below code is for concatenating two dataframes column wise
Program :
import pandas as pd
df1 = pd.DataFrame({ #creating a dataframe
'A': [1, 2],
'B': [3, 4]
})
df2 = pd.DataFrame({ #creating another dataframe
'A': [5, 6],
'B': [7, 8]
})
result = pd.concat([df1, df2], axis=1) # Concatenate along column (axis=1)
print(result) #printing the resultant dataframe
Output:
A B A B 0 1 3 5 7 1 2 4 6 8
Here, we can understand that the two dataframes are concatenated column wise . This is by specifing axis = 1 .