Using Group By with Where Condition in PandasPandas is a widely used data analysis and manipulation library in Python. It provides an easy way to handle and analyze large amounts of data.

In this article, we will focus on using group by with where condition in Pandas, which is an essential feature for data analysis.

Example of using query() function to calculate mean value of points grouped by position, where team is equal to ‘A’

To start, let’s consider an example of using the query() function to calculate the mean value of points grouped by position, where the team is equal to ‘A’.

Assuming we have a pandas DataFrame called “basketball_data”, we can use the groupby() function to group the data according to the column “position”. “`

grouped_data = basketball_data.groupby(‘position’)

“`

Next, we can use the query() function to include a where condition to filter the data where the team is equal to ‘A’.

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team_A_data = grouped_data.query(‘team == “A”‘)

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Lastly, we can use the mean() function to calculate the mean value of the points in the filtered data. “`

mean_points = team_A_data[‘points’].mean()

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This will give us the mean value of points grouped by position, where the team is equal to ‘A’.

Additionally, we can reset the index of the resulting DataFrame using the reset_index() function to make it easier to work with. “`

mean_points_data = team_A_data[‘points’].mean().reset_index(name=”mean_points”)

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This will give us a pandas DataFrame which shows the mean value of points grouped by position, where the team is equal to ‘A’.

Using & operator in query() function for multiple conditions

Now let’s consider a scenario where we want to filter the data by multiple conditions. We can use the & operator in the query() function to include multiple conditions.

For instance, we may want to filter the data where the team is equal to ‘A’ and the position is either ‘Point Guard’ or ‘Shooting Guard’.

“`

team_A_guard_data = grouped_data.query(‘team == “A” & (position == “Point Guard” | position == “Shooting Guard”)’)

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This will give us a pandas DataFrame which shows the data where the team is equal to ‘A’ and the position is either ‘Point Guard’ or ‘Shooting Guard’.

## Working with a Sample Pandas DataFrame

Creating a sample DataFrame for basketball players’ information

Let’s now consider a scenario where we want to create a sample DataFrame for basketball players’ information. We can use the pandas DataFrame function to create a DataFrame.

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## import pandas as pd

player_data = pd.DataFrame({

‘name’: [‘John’, ‘Sam’, ‘Mike’, ‘David’, ‘Joe’],

‘age’: [25, 28, 23, 21, 30],

‘height’: [6.2, 6.3, 6.1, 6.0, 6.5],

‘position’: [‘Center’, ‘Small Forward’, ‘Power Forward’, ‘Shooting Guard’, ‘Point Guard’],

‘team’: [‘A’, ‘B’, ‘A’, ‘B’, ‘A’],

‘points’: [15, 10, 12, 8, 20]

})

“`

This will create a pandas DataFrame with columns for name, age, height, position, team, and points for each basketball player.

## Displaying and manipulating data in the sample DataFrame

We can then display and manipulate the data in the sample DataFrame using pandas functions.

For example, we can use the head() function to show the first five rows of the DataFrame.

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player_data.head()

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This will display the first five rows of the DataFrame. We can also filter the data based on specific criteria, such as players who play for team ‘A’.

“`

team_A_players = player_data[player_data[‘team’] == ‘A’]

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This will create a new DataFrame with only the players who play for team ‘A’. We can also sort the data by specific columns, such as points scored.

“`

sorted_data = player_data.sort_values(‘points’, ascending=False)

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This will sort the DataFrame by ‘points’ column in descending order. Conclusion:

In conclusion, Pandas provides a powerful set of tools for working with data in Python.

The groupby() function and query() function are just a few examples of the powerful features that pandas provides for data analysis. Similarly, creating sample DataFrames and manipulating data using pandas functions is an essential step in data analysis.

These tools can help you perform complex data analysis tasks with relative ease and speed. Remember to keep practicing and exploring the capabilities of pandas to improve your data analysis skills.

In summary, this article has emphasized the importance of using group by with where condition in Pandas for effective data analysis. Specifically, we looked at how to use the query() function to filter data based on specific conditions, as well as how to manipulate and analyze sample Pandas DataFrames using various Pandas functions.

The key takeaway is that Pandas is a powerful tool for performing complex data analysis tasks, and practicing with these features can help to improve data analysis skills. In conclusion, it is important to continue exploring and learning the capabilities of Pandas to become better in data analysis.