Sometimes in order to analyze the Dataframe more accurately, we need to split it into 2 or more parts. The Pandas provide the feature to split Dataframe according to column index, row index, and column values, etc.
Let’ see how to Split Pandas Dataframe by column value in Python?
Now, let’s create a Dataframe:
villiers
Python3
# importing pandas libraryimport pandas as pd# Initializing the nested list with Data-setplayer_list = [['M.S.Dhoni', 36, 75, 5428000], ['A.B.D Villiers', 38, 74, 3428000], ['V.Kholi', 31, 70, 8428000], ['S.Smith', 34, 80, 4428000], ['C.Gayle', 40, 100, 4528000], ['J.Root', 33, 72, 7028000], ['K.Peterson', 42, 85, 2528000]]# creating a pandas dataframedf = pd.DataFrame(player_list, columns = ['Name', 'Age', 'Weight', 'Salary'])# show the dataframedf |
Output:
Method 1: Using boolean masking approach.
This method is used to print only that part of dataframe in which we pass a boolean value True.
Example 1:
Python3
# importing pandas libraryimport pandas as pd# Initializing the nested list with Data-setplayer_list = [['M.S.Dhoni', 36, 75, 5428000], ['A.B.D Villiers', 38, 74, 3428000], ['V.Kholi', 31, 70, 8428000], ['S.Smith', 34, 80, 4428000], ['C.Gayle', 40, 100, 4528000], ['J.Root', 33, 72, 7028000], ['K.Peterson', 42, 85, 2528000]]# creating a pandas dataframedf = pd.DataFrame(player_list, columns = ['Name', 'Age', 'Weight', 'Salary'])# splitting the dataframe into 2 parts# on basis of 'Age' column values# using Relational operatordf1 = df[df['Age'] >= 37]# printing df1df1 |
Output:
Python3
df2 = df[df['Age'] < 37]# printing df2df2 |
Output:
In the above example, the data frame ‘df’ is split into 2 parts ‘df1’ and ‘df2’ on the basis of values of column ‘Age‘.
Example 2:
Python3
# importing pandas libraryimport pandas as pd# Initializing the nested list with Data-setplayer_list = [['M.S.Dhoni', 36, 75, 5428000], ['A.B.D Villiers', 38, 74, 3428000], ['V.Kholi', 31, 70, 8428000], ['S.Smith', 34, 80, 4428000], ['C.Gayle', 40, 100, 4528000], ['J.Root', 33, 72, 7028000], ['K.Peterson', 42, 85, 2528000]]# creating a pandas dataframedf = pd.DataFrame(player_list, columns = ['Name', 'Age', 'Weight', 'Salary'])# splitting the dataframe into 2 parts# on basis of 'Weight' column valuesmask = df['Weight'] >= 80df1 = df[mask]# invert the boolean valuesdf2 = df[~mask]# printing df1df1 |
Output:
Python3
# printing df2df2 |
Output:
In the above example, the data frame ‘df’ is split into 2 parts ‘df1’ and ‘df2’ on the basis of values of column ‘Weight‘.
Method 2: Using Dataframe.groupby().
This method is used to split the data into groups based on some criteria.
Example:
Python3
# importing pandas libraryimport pandas as pd# Initializing the nested list with Data-setplayer_list = [['M.S.Dhoni', 36, 75, 5428000], ['A.B.D Villiers', 38, 74, 3428000], ['V.Kholi', 31, 70, 8428000], ['S.Smith', 34, 80, 4428000], ['C.Gayle', 40, 100, 4528000], ['J.Root', 33, 72, 7028000], ['K.Peterson', 42, 85, 2528000]]# creating a pandas dataframedf = pd.DataFrame(player_list, columns = ['Name', 'Age', 'Weight', 'Salary'])# splitting the dataframe into 2 parts# on basis of 'Salary' column values# using dataframe.groupby() functiondf1, df2 = [x for _, x in df.groupby(df['Salary'] < 4528000)]# printing df1df1 |
Output:
Python3
# printing df2df2 |
Output:
In the above example, the data frame ‘df’ is split into 2 parts ‘df1’ and ‘df2’ on the basis of values of column ‘Salary‘.
