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In Pandas, a nested dataframe is created by combining multiple dataframes into one. This can be done by using the concat() or merge() functions with the right set of parameters. The dataframes must have the same column names and data types in order for the nesting to be successful. Additionally, it is important to note that the order of the dataframes being combined will determine the output of the new nested dataframe.
You can use the following syntax to nest multiple pandas DataFrames within another DataFrame:
df_all = pd.DataFrame({'idx':[1,2,3], 'dfs':[df1, df2, df3]})
This particular example nests three DataFrames (df1, df2, df3) inside a larger DataFrame called df_all.
You can then use the following syntax to access one of the specific nested DataFrames:
#display first nested DataFrame print(df_all['dfs'].iloc[0])
The following example shows how to use this syntax in practice.
Example: Create Nested DataFrame in Pandas
Suppose we have three pandas DataFrames:
import pandas as pd #create first DataFrame df1 = pd.DataFrame({'item': ['A', 'B', 'C', 'D', 'E'], 'sales': [18, 22, 19, 14, 30]}) print(df1) item sales 0 A 18 1 B 22 2 C 19 3 D 14 4 E 30 #create second DataFrame df2 = pd.DataFrame({'item': ['F', 'G', 'H', 'I', 'J'], 'sales': [10, 12, 13, 13, 19]}) print(df2) item sales 0 F 10 1 G 12 2 H 13 3 I 13 4 J 19 #create third DataFrame df3 = pd.DataFrame({'item': ['K', 'L', 'M', 'N', 'O'], 'sales': [41, 22, 28, 25, 18]}) print(df3) item sales 0 K 41 1 L 22 2 M 28 3 N 25 4 O 18
Now suppose that we would like to create one big DataFrame to hold all three of these DataFrames.
We can use the following syntax to do so:
df_all = pd.DataFrame({'idx':[1,2,3], 'dfs':[df1, df2, df3]})
We can then use the pandas iloc function to access specific nested DataFrames.
For example, we can use the following syntax to access the first nested DataFrame:
#display first nested DataFrame print(df_all['dfs'].iloc[0]) item sales 0 A 18 1 B 22 2 C 19 3 D 14 4 E 30
Or we could use the following syntax to access the second nested DataFrame:
#display second nested DataFrame print(df_all['dfs'].iloc[1]) item sales 0 F 10 1 G 12 2 H 13 3 I 13 4 J 19
The following tutorials explain how to perform other common functions in pandas: