Adventures in Machine Learning

Filtering Pandas DataFrames: The Power of NOT IN

Filtering Pandas DataFrames with “NOT IN”

Data is the new oil, and in todays world, success is highly dependent on our ability to extract valuable insights from this data. But what do we do when we have large amounts of data and want to extract only a specific subset of it?

This is where filtering comes in, and pandas, a popular data manipulation library in Python, has some of the most efficient filtering functions. In this article, we will delve into the topic of filtering pandas DataFrames with “NOT IN” and provide you with some additional resources for other possible filtering operations.

“NOT IN” Filter with One Column

As the name suggests, the “NOT IN” filter selects rows not found in a specified list of values. We can easily filter with the `~df[‘team’].isin(values_list)` code, where `df` is our pandas DataFrame and `team` is the column we want to filter on.

For example, if we wanted to exclude all rows that have a certain team name, we can do the following:

“`

import pandas as pd

# Create a dataframe

df = pd.DataFrame({

‘team’: [‘Bulls’, ‘Lakers’, ‘Clippers’, ‘Nets’],

‘wins’: [31, 24, 27, 35],

‘losses’: [15, 21, 18, 10]

})

# List of team names to exclude

exclude_list = [‘Lakers’]

# Filter out rows

filtered_df = df[~df[‘team’].isin(exclude_list)]

“`

The `filtered_df` DataFrame will exclude the Lakers team from the original DataFrame. We can also filter a DataFrame based on the condition on multiple columns.

“NOT IN” Filter with Multiple Columns

To filter a DataFrame based on a “NOT IN” condition on multiple columns, we can use `~df[[‘star_team’, ‘backup_team’]].isin(values_list).any(axis=1)`. The `.any(axis=1)` function ensures that all values of a specific row satisfy the condition applied to the columns in the list.

For example, let’s say we want to exclude the rows that have either the Lakers or Warriors as both star and backup teams. “`

import pandas as pd

# Create a dataframe

df = pd.DataFrame({

‘star_team’: [‘Bulls’, ‘Lakers’, ‘Clippers’, ‘Nets’],

‘backup_team’: [‘Warriors’, ‘Heat’, ‘Celtics’, ‘Rockets’],

‘wins’: [31, 24, 27, 35],

‘losses’: [15, 21, 18, 10]

})

# List of teams to exclude

exclude_list = [‘Lakers’, ‘Warriors’]

# Filter out rows

filtered_df = df[~df[[‘star_team’, ‘backup_team’]].isin(exclude_list).any(axis=1)]

“`

The `filtered_df` DataFrame will exclude all rows containing both Lakers and Warriors teams as either star or backup teams.

Common Filtering Operations in pandas

Apart from “NOT IN” filters, pandas provides a wide variety of other filtering operations to help us extract only the data we need. Some common filtering operations in pandas include:

1.

Conditional filtering with boolean indexing: `df[df[‘column’] > 5]`

2. Filtering with `.loc` and `.iloc`: `df.loc[df[‘column’] > 5, [‘column1’, ‘column2’]]`

3.

Filtering with the `.query()` method: `df.query(‘column > 5’)`

4. Filtering with the `.isin()` method: `df[df[‘column’].isin([1,2,3])]`

Other

Common Filtering Operations in pandas

There are several tutorials and courses available online that teach different pandas filtering operations.

Some common ones include:

1. DataCamps Manipulating DataFrames course: This course covers all the basics of pandas data manipulation, including filtering, sorting, and aggregating data.

2. pandas documentation: The official pandas documentation is well-documented and provides examples of different filtering operations with a detailed explanation of each method.

3. W3Schools pandas tutorial: W3Schools provides a comprehensive tutorial that covers the basics of pandas data manipulation, including filtering.

In conclusion, pandas offers a wide range of filtering operations to help us extract only the data we need from large datasets. By using techniques such as “NOT IN” filtering with one or multiple columns, we can easily exclude specific rows from our DataFrame.

Moreover, several resources, including courses, tutorials, and documentation, are available to help us learn and apply these techniques to our data manipulation tasks. In summary, filtering Pandas DataFrames with “NOT IN” is an essential technique that can help extract significant insights from large datasets.

This technique involves excluding specific rows based on a given list of values. We can filter based on one or multiple columns, and pandas offers several common filtering operations such as boolean indexing, `.loc`, `.iloc`, and `.isin()`.

Additionally, several learning resources such as courses, tutorials, and documentation are readily available to help us learn about different filtering operations and leverage them in our data manipulation tasks. By applying filtering techniques, we can gain valuable insights and ultimately make data-driven decisions more accurately and efficiently.

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