In this article, we will discuss rbind() in python.
Method 1: Use rbind() function with equal columns
Here we have to take 2 dataframes with equal columns and apply concat() function. This will combine the rows based on columns.
Syntax:
pandas.concat([dataframe1, dataframe2])
where
- dataframe1 is the first dataframe
- dataframe2 is the second dataframe
Example:
Python3
# import pandas module import pandas as pd
# create first dataframe data1 = pd.DataFrame({'fruits': ['apple', 'guava', 'mango', 'banana'],
'cost': [34, 56, 65, 45]})
# create second dataframe data2 = pd.DataFrame({'fruits': ['cuatard apple', 'guava', 'mango', 'papaya'],
'cost': [314, 86, 65, 51]})
# concat two columns pd.concat([data1, data2]) |
Output:
Method 2: Use rbind() function with unequal columns
Here the two dataframes columns are not equal, In this scenario, the unmatched column will get NAN replaced rows in its column.
Syntax:
pandas.concat([dataframe1, dataframe2])
where,
- dataframe1 is the first dataframe
- dataframe2 is the second dataframe
Example:
Python3
# import pandas module import pandas as pd
# create first dataframe with 2 columns data1 = pd.DataFrame({'fruits': ['apple', 'guava', 'mango', 'banana'],
'cost': [34, 56, 65, 45]})
# create second dataframe with 3 columns data2 = pd.DataFrame({'fruits': ['cuatard apple', 'guava', 'mango', 'papaya'],
'cost': [314, 86, 65, 51],
'city': ['guntur', 'tenali', 'ponnur', 'hyd']})
# concat two columns pd.concat([data1, data2]) |
Output:
Here we observed that the index of the rows again starts from 0, in order to avoid this, we have to use the .reset_index() method. This will reset the index of the new dataframe.
Syntax:
pandas.concat([dataframe1, dataframe2]).reset_index(drop=True)
Example:
Python3
# import pandas module import pandas as pd
# create first dataframe with 2 columns data1 = pd.DataFrame({'fruits': ['apple', 'guava', 'mango', 'banana'],
'cost': [34, 56, 65, 45]})
# create second dataframe with 3 columns data2 = pd.DataFrame({'fruits': ['cuatard apple', 'guava', 'mango', 'papaya'],
'cost': [314, 86, 65, 51],
'city': ['guntur', 'tenali', 'ponnur', 'hyd']})
# concat two columns pd.concat([data1, data2]).reset_index(drop=True)
|
Output: