How do I select rows from a DataFrame based on column values?

You can use the DataFrame.loc method to select rows from a DataFrame based on column values.

Here is an example:

Select rows from a DataFrame based on column values in Python

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})

# Select rows where column 'A' has a value greater than 1
df_subset = df.loc[df['A'] > 1]

print(df_subset)

The output of this code will be:

A  B  C
1  2  5  8
2  3  6  9

You can also use multiple conditions to select rows using the & and | operators to specify logical AND and OR, respectively. For example:

Use multiple conditions to select rows from a DataFrame in Python

# Select rows where column 'A' has a value greater than 1 and column 'B' has a value less than 6
df_subset = df.loc[(df['A'] > 1) & (df['B'] < 6)]

print(df_subset)

The output of this code will be:

A  B  C
1  2  5  8

You can also use the .isin() method to select rows based on whether the value in a certain column is in a list of values. For example:

Using the isin() method to select rows based on whether the value in a certain column is in a list of values in Python DataFrame

# Select rows where column 'A' has a value in the list [1, 3]
df_subset = df.loc[df['A'].isin([1, 3])]

print(df_subset)

The output of this code will be:

A  B  C
0  1  4  7
2  3  6  9