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Stacking multiple pandas Dataframes is the process of merging or combining several dataframes into one by aligning rows and columns. This process is often used for combining data from different sources into a single dataframe, allowing for easier analysis and processing of the data. It can also be used to merge two or more dataframes that contain the same or similar data, but in different formats.
Often you may wish to stack two or more pandas DataFrames. Fortunately this is easy to do using the pandas concat() function.
This tutorial shows several examples of how to do so.
Example 1: Stack Two Pandas DataFrames
The following code shows how to “stack” two pandas DataFrames on top of each other and create one DataFrame:
import pandas as pd #create two DataFrames df1 = pd.DataFrame({'player': ['A', 'B', 'C', 'D', 'E'], 'points':[12, 5, 13, 17, 27]}) df2 = pd.DataFrame({'player': ['F', 'G', 'H', 'I', 'J'], 'points':[24, 26, 27, 27, 12]}) #"stack" the two DataFrames together df3 = pd.concat([df1,df2], ignore_index=True) #view resulting DataFrame df3 player points 0 A 12 1 B 5 2 C 13 3 D 17 4 E 27 5 F 24 6 G 26 7 H 27 8 I 27 9 J 12
Example 2: Stack Three Pandas DataFrames
Similar code can be used to stack three pandas DataFrames on top of each other to create one DataFrame:
import pandas as pd #create three DataFrames df1 = pd.DataFrame({'player': ['A', 'B', 'C', 'D', 'E'], 'points':[12, 5, 13, 17, 27]}) df2 = pd.DataFrame({'player': ['F', 'G', 'H', 'I', 'J'], 'points':[24, 26, 27, 27, 12]}) df3 = pd.DataFrame({'player': ['K', 'L', 'M', 'N', 'O'], 'points':[9, 5, 5, 13, 17]}) #"stack" the two DataFrames together df4 = pd.concat([df1,df2, df3], ignore_index=True) #view resulting DataFrame df4 player points 0 A 12 1 B 5 2 C 13 3 D 17 4 E 27 5 F 24 6 G 26 7 H 27 8 I 27 9 J 12 10 K 9 11 L 5 12 M 5 13 N 13 14 O 17
The Importance of ignore_index
Note that in the previous examples we used ignore_index=True.
This tells pandas to ignore the index numbers in each DataFrame and to create a new index ranging from 0 to n-1 for the new DataFrame.
For example, consider what happens when we don’t use ignore_index=True when stacking the following two DataFrames:
import pandas as pd #create two DataFrames with indices df1 = pd.DataFrame({'player': ['A', 'B', 'C', 'D', 'E'], 'points':[12, 5, 13, 17, 27]}, index=[0, 1, 2, 3, 4]) df2 = pd.DataFrame({'player': ['F', 'G', 'H', 'I', 'J'], 'points':[24, 26, 27, 27, 12]}, index=[2, 4, 5, 6, 9]) #stack the two DataFrames together df3 = pd.concat([df1,df2]) #view resulting DataFrame df3 player points 0 A 12 1 B 5 2 C 13 3 D 17 4 E 27 2 F 24 4 G 26 5 H 27 6 I 27 9 J 12
The resulting DataFrame kept its original index values from the two DataFrames.
Thus, you should typically use ignore_index=True when stacking two DataFrames unless you have a specific reason for keeping the original index values.
The following tutorials explain how to perform other common tasks in Pandas:
How to Add an Empty Column to a Pandas DataFrame
How to Insert a Column Into a Pandas DataFrame
How to Export a Pandas DataFrame to Excel