Adventures in Machine Learning

Mastering Pandas: Dropping Rows with Multiple Conditions

Dropping Rows Based on Multiple Conditions in a Pandas DataFrame

No one likes working with messy data, and one of the biggest sources of frustration can be trying to filter out unwanted rows from a DataFrame. Fortunately, Pandas makes it easy to drop rows based on certain conditions, allowing us to focus on the data that really matters.

In this article, well explore two methods for dropping rows based on multiple conditions: dropping rows that meet one of several conditions and dropping rows that meet several conditions. Method 1: Dropping Rows that Meet One of Several Conditions

First, lets assume we have a DataFrame that contains data on basketball teams, including the number of assists they made in each game.

We want to drop all rows that either belong to the Boston Celtics or have less than 20 assists. To do this, well use the | (or) operator to combine our two conditions.

“`

import pandas as pd

df = pd.DataFrame({‘Team’: [‘Boston Celtics’, ‘LA Lakers’, ‘Miami Heat’, ‘Toronto Raptors’, ‘Phoenix Suns’],

‘Assists’: [12, 23, 18, 21, 15]})

df.drop(df[(df[‘Team’] == ‘Boston Celtics’) | (df[‘Assists’] < 20)].index, inplace=True)

print(df)

“`

In this code, we first create our DataFrame using the `pd.DataFrame()` function from Pandas. We then use the `drop()` method to drop all rows that meet one of several conditions.

We use the `.index` attribute to get the indexes of the rows we want to drop, and then set `inplace=True` to tell Pandas to modify our original DataFrame instead of returning a new one. The output should show a DataFrame with only the rows that don’t meet either of our conditions:

“`

Team Assists

LA Lakers 23

Toronto Raptors 21

“`

Now let’s take a look at the next method.

Method 2: Dropping Rows that Meet Several Conditions

Lets assume we have a new DataFrame that contains data on football teams, including the number of goals theyve scored and the number of goals theyve conceded in each game. We want to drop all rows that belong to teams that have scored less than 20 goals and conceded more than 10 goals.

To do this, well use the & (and) operator to combine our two conditions. “`

df = pd.DataFrame({‘Team’: [‘Barcelona’, ‘Real Madrid’, ‘Manchester United’, ‘Bayern Munich’, ‘Liverpool’],

‘Goals Scored’: [21, 18, 15, 23, 25],

‘Goals Conceded’: [9, 14, 18, 12, 11]})

df.drop(df[(df[‘Goals Scored’] < 20) & (df['Goals Conceded'] > 10)].index, inplace=True)

print(df)

“`

In this code, we first create our DataFrame using the `pd.DataFrame()` function from Pandas. We then use the `drop()` method to drop all rows that meet several conditions.

We use the `.index` attribute to get the indexes of the rows we want to drop, and then set `inplace=True` to modify our original DataFrame. The output will show a DataFrame with only the rows that don’t meet both of our conditions:

“`

Team Goals Scored Goals Conceded

Barcelona 21 9

Real Madrid 18 14

Manchester United 15 18

Bayern Munich 23 12

Liverpool 25 11

“`

Conclusion

Cleaning up messy data is essential for anyone working with data, whether youre an analyst, a data scientist, or just a curious person who likes to explore data. Pandas provides a powerful toolset for filtering and manipulating data, including the ability to drop rows based on multiple conditions.

By using the tips and tricks weve provided here, youll be well on your way to mastering Pandas and cleaning up even the messiest data sets. 3) Example 2: Drop Rows that Meet Several Conditions

Now, let’s take another example to demonstrate the second method of dropping rows based on multiple conditions.

Assume that we have a DataFrame containing data on football teams, including the players’ name, jersey number, the number of goals they have scored, and the number of assists they have provided. We want to drop all rows that meet both of the following conditions:

– The player has scored less than 5 goals

– The player has provided less than 2 assists

“`

import pandas as pd

df = pd.DataFrame({

‘Player’: [‘Messi’, ‘Ronaldo’, ‘Neymar’, ‘Mbappe’, ‘Haaland’],

‘Jersey’: [10, 7, 10, 7, 9],

‘Goals’: [21, 18, 10, 9, 8],

‘Assists’: [12, 11, 3, 6, 2]

})

df.drop(df[(df[‘Goals’] < 5) & (df['Assists'] < 2)].index, inplace=True)

print(df)

“`

In the code above, we’re using the `&` (and) operator to combine the two conditions. We’re selecting all the rows that meet both conditions simultaneously.

The output will show the DataFrame that contains rows with players who have met at least one of the conditions we specified:

“`

Player Jersey Goals Assists

Messi 10 21 12

Ronaldo 7 18 11

Neymar 10 10 3

Mbappe 7 9 6

“`

We have all the rows that do not meet both of the specified conditions.

4) Additional Resources

Pandas is a powerful library in Python that provides various data manipulation and analysis tools. It’s widely used in both academic and industrial fields.

In case you would like to explore more of its capabilities, here are some additional resources that can help you master it:

1. Official Pandas documentation – the official source for documentation, tutorials, and guides on Pandas.

2. Pandas Cheat Sheet – A one-page cheat sheet summarizing the most important Pandas functions and methods.

3. Pandas Tutorials – Collection of free online Pandas tutorials for beginners and advanced learners.

4. 10 minutes to pandas – A quick guide to Pandas, demonstrating some of its major capabilities.

5. Data School – YouTube channel with in-depth tutorials and tips for using Pandas and other data analysis tools in Python.

By exploring these resources, you can learn how to use Pandas for common operations like merging, manipulating, filtering, selecting, and aggregating data, and for more specialized tasks and analysis. By mastering Pandas, you’ll be able to structure and analyze large datasets, extract insights, make informed decisions, and gain a competitive edge in a data-driven world.

In conclusion, dropping rows based on multiple conditions is an essential tool for data cleaning and manipulation using Pandas. This article has covered two methods for dropping rows that meet one or more conditions using the | (or) and & (and) operators.

By applying these methods to a variety of examples, we have shown how to effectively filter a DataFrame based on multiple criteria. Additionally, we have provided a list of resources for further learning about Pandas.

By mastering this important data manipulation technique and other functions offered by Pandas, data scientists and analysts can effectively clean and explore datasets, making informed data-driven decisions to support their field of work.

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