Subset Rows & Columns of pandas DataFrame in Python (2 Examples)
In this tutorial you’ll learn how to subset the rows and columns of a pandas DataFrame in Python.
Setting up the Examples
import pandas as pd # Load pandas library |
import pandas as pd # Load pandas library
my_df = pd.DataFrame({'A':range(1, 6), # Constructing a pandas DataFrame 'B':range(7, 12), 'C':range(13, 18), 'D':range(19, 24)}) print(my_df) # A B C D # 0 1 7 13 19 # 1 2 8 14 20 # 2 3 9 15 21 # 3 4 10 16 22 # 4 5 11 17 23 |
my_df = pd.DataFrame({'A':range(1, 6), # Constructing a pandas DataFrame 'B':range(7, 12), 'C':range(13, 18), 'D':range(19, 24)}) print(my_df) # A B C D # 0 1 7 13 19 # 1 2 8 14 20 # 2 3 9 15 21 # 3 4 10 16 22 # 4 5 11 17 23
Example 1: Using a Logical Condition to Subset a pandas DataFrame
df1 = my_df.loc[my_df['B'] <= 9] # Create pandas DataFrame subset print(df1) # A B C D # 0 1 7 13 19 # 1 2 8 14 20 # 2 3 9 15 21 |
df1 = my_df.loc[my_df['B'] <= 9] # Create pandas DataFrame subset print(df1) # A B C D # 0 1 7 13 19 # 1 2 8 14 20 # 2 3 9 15 21
Example 2: Using a Random Process to Subset a pandas DataFrame
import numpy # Import numpy |
import numpy # Import numpy
numpy.random.seed(23466463) # Create pandas DataFrame subset df2 = my_df.sample(frac = 0.75) print(df2) # A B C D # 1 2 8 14 20 # 4 5 11 17 23 # 3 4 10 16 22 # 2 3 9 15 21 |
numpy.random.seed(23466463) # Create pandas DataFrame subset df2 = my_df.sample(frac = 0.75) print(df2) # A B C D # 1 2 8 14 20 # 4 5 11 17 23 # 3 4 10 16 22 # 2 3 9 15 21
Further Resources & Related Articles
Here, you can find some additional resources on topics such as counting, variables, and lists.