Combining DataFrames with Pandas
Last Updated :
08 Nov, 2022
Pandas DataFrame consists of three principal components, the data, rows, and columns. To combine these DataFrames, pandas provides multiple functions like concat() and append().
Method #1: Using concat() method
Initially, creating two datasets and converting them into dataframes.
Python3
import pandas as pd
data1 = {
'Serial_No.': ['1', '2', '3', '4', '5'],
'First': ['F0', 'F1', 'F2', 'F3', 'F4'],
'Second': ['S0', 'S1', 'S2', 'S3', 'S4'],
}
df1 = pd.DataFrame(data1, columns=['Serial_No.',
'First',
'Second'])
df1
data2 = {
'Serial_No.': ['6', '7', '8', '9', '10'],
'First': ['F10', 'F11', 'F12', 'F13', 'F14'],
'Second': ['S10', 'S11', 'S12', 'S13', 'S14'],
}
df2 = pd.DataFrame(data2, columns=['Serial_No.',
'First',
'Second'])
df2
|
Output:
Now, concatenating the two dataframes, we will use concat() to combine two dataframes. If ignore_index = True the index of df will be in a continuous order.
Python3
df = pd.concat([df1, df2], ignore_index=True)
df
|
Output:
Using keys we can specify the labels of the dataframes.
Python3
frames = [df1, df2]
df_keys = pd.concat(frames, keys=['x', 'y'])
df_keys
|
Output:
Method #2: Using append() method
Initially, creating two datasets and converting them into dataframes.
Python3
import pandas as pd
data1 = {
'Serial_No.': ['1', '2', '3', '4', '5'],
'First': ['F0', 'F1', 'F2', 'F3', 'F4'],
'Second': ['S0', 'S1', 'S2', 'S3', 'S4'],
}
df1 = pd.DataFrame(data1, columns=['Serial_No.',
'First',
'Second'])
df1
data2 = {
'Serial_No.': ['6', '7', '8', '9', '10'],
'First': ['F10', 'F11', 'F12', 'F13', 'F14'],
'Second': ['S10', 'S11', 'S12', 'S13', 'S14'],
}
df2 = pd.DataFrame(data2, columns=['Serial_No.',
'First',
'Second'])
df2
|
Output:
The dataframe.append() method performs the operation of combining two dataframes similar to that of the concat() method.
Python3
result = df1.append(df2, sort=False, ignore_index=True)
result
|
Output:
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