How to filter Pandas dataframe using 'in' and 'not in' like in SQL

You can filter a Pandas DataFrame using the isin() and ~(not in) methods.

Here is an example of how to filter a DataFrame for rows where a column has a value that is in a list:

Filter a DataFrame for rows where a column has a value that is in a list in Python

import pandas as pd

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

# create a list of values to filter for
values_to_filter = [2, 3]

# use the isin() method to filter the DataFrame
filtered_df = df[df['A'].isin(values_to_filter)]

print(filtered_df)

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This will output:

---

A  B
1  2  5
2  3  6

Here is an example of how to filter a DataFrame for rows where a column has a value that is not in a list:

Filter a DataFrame for rows where a column has a value that is not in a list in Python

import pandas as pd

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

# create a list of values to filter for
values_to_filter = [2, 3]

# use the ~ (not in) operator along with the isin() method to filter the DataFrame
filtered_df = df[~df['A'].isin(values_to_filter)]

print(filtered_df)

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This will output:

---

A  B
0  1  4

You can also use multiple conditions by & for and and | for or.

Use multiple conditions in filtering a Pandas DataFrame in Python

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

# create a list of values to filter for
values_to_filter = [2, 3]

# use the ~ (not in) operator along with the isin() method to filter the DataFrame
filtered_df = df[(df['A'].isin(values_to_filter)) & (df['C']>8)]

print(filtered_df)

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This will output:

---

A  B  C
2  3  6  9