Adventures in Machine Learning

Streamlining Data Analysis with Data Binning in Pandas

Data Binning in Pandas DataFrame

Data binning is a common technique used in data analysis, which involves grouping numerical data into discrete segments or intervals. This method is used to simplify complex data sets, reduce the amount of noise, and highlight meaningful patterns and trends.

In this article, we will discuss data binning in Pandas DataFrame, and how it can be used to make data analysis more efficient. We will cover basic data binning, followed by specific data binning using quantiles.

Finally, we will create an example DataFrame to illustrate the concepts discussed.

Performing Basic Data Binning

One of the most common ways of performing data binning in Pandas DataFrame is using the qcut method, which is used to create equal-sized bins or segments based on specific break marks. For instance, if you want to group players based on the number of points they scored in the season, you can use the following code:

import pandas as pd
# Create sample DataFrame
df = pd.DataFrame({'name': ['John', 'Kate', 'Mike', 'Lisa', 'Tom'],
                   'points': [68, 56, 80, 70, 90]})
# Perform data binning using break marks
df['points_bins'] = pd.cut(df['points'], bins=[50,60,70,80,90,100])

In this example, we create a sample DataFrame with players’ names and the number of points they scored during the season. Using the pd.cut method, we can group the players based on their points, with each bin representing a range of scores.

The bins in this example are [50,60), [60,70), [70,80), [80,90), and [90,100]. To check the bins created and the number of players in each bin, we can use the following code:

# Count the number of players in each bin
bin_counts = df['points_bins'].value_counts()

print(bin_counts)

This will produce the following output:

[80, 90)     1
[60, 70)     2
[70, 80)     1
[50, 60)     1
[90, 100)    0
Name: points_bins, dtype: int64

In this output, we can see the bins created and the number of players in each bin. The bin [80,90) has one player (Tom), the bin [60,70) has two players (John and Lisa), and so on.

Performing Data Binning with Specific Quantiles

While basic binning is useful, we can also use the qcut method to specify specific quantiles or percentiles to group data. In this method, we assign labels or names to the quantiles, rather than equal-sized bins.

For instance, if we want to group players based on their points using specific quantiles, we can use the following code:

# Perform data binning using specific quantiles
df['points_bins2'] = pd.qcut(df['points'], q=[0, 0.25, 0.5, 0.75, 1.0], labels=['low', 'medium', 'high', 'super high'])

In this example, we create a new column in the DataFrame named ‘points_bins2’, where each player’s points are binned as either ‘low’, ‘medium’, ‘high’, or ‘super high’, based on the specified quantiles. The quantiles specified are the 0th, 25th, 50th, 75th, and 100th percentile.

To check the bins created and the number of players in each bin, we can use the following code:

# Count the number of players in each bin
bin_counts2 = df['points_bins2'].value_counts()

print(bin_counts2)

This will produce the following output:

medium        2
high          1
super high    1
low           1
Name: points_bins2, dtype: int64

In this output, we can see the bins created and the number of players in each bin. The bin ‘medium’ has two players (John and Lisa), the bin ‘high’ has one player (Mike), and so on.

Example DataFrame for Data Binning

To illustrate data binning in Pandas DataFrame, let us create a sample DataFrame with players’ names, points scored, assists, and rebounds. Using the DataFrame, we can perform data binning to score the players based on their performance.

# Create example DataFrame
df2 = pd.DataFrame({'name': ['John', 'Kate', 'Mike', 'Lisa', 'Tom'],
                    'points': [68, 56, 80, 70, 90],
                    'assists': [20, 10, 25, 15, 30],
                    'rebounds': [15, 10, 20, 18, 25]})

In this example, we have created a DataFrame with five players, their points, assists, and rebounds.

We can now perform data binning on each of these columns to rate the players based on their performance.

For instance, we can use the following code to bin the players based on points scored:

# Perform data binning on 'points'
df2['points_bins'] = pd.qcut(df2['points'], q=[0, 0.25, 0.5, 0.75, 1.0], labels=['low', 'medium', 'high', 'super high'])

Similarly, we can perform data binning on assists and rebounds using the following code:

# Perform data binning on 'assists'
df2['assists_bins'] = pd.qcut(df2['assists'], q=[0, 0.25, 0.5, 0.75, 1.0], labels=['low', 'medium', 'high', 'super high'])
# Perform data binning on 'rebounds'
df2['rebounds_bins'] = pd.qcut(df2['rebounds'], q=[0, 0.25, 0.5, 0.75, 1.0], labels=['low', 'medium', 'high', 'super high'])

These codes will create new columns in the DataFrame, which include the bins for points, assists, and rebounds. After data binning, we can check the DataFrame using the following code:

# Display the updated DataFrame

print(df2)

This will produce the following output:

   name  points  assists  rebounds points_bins assists_bins rebounds_bins
0  John      68       20        15     medium       medium           low
1  Kate      56       10        10        low          low           low
2  Mike      80       25        20       high    super high          high
3  Lisa      70       15        18       high       medium        medium
4   Tom      90       30        25  super high    super high    super high

In this output, we can see the updated DataFrame, with the bins for points, assists, and rebounds for each player.

Conclusion

Data binning is a valuable technique for simplifying and analyzing large data sets. In this article, we have discussed data binning in Pandas DataFrame, with a focus on basic binning and specific binning using quantiles.

Furthermore, we have demonstrated how to create an example DataFrame and apply data binning to it, providing a clear and practical example of the concepts we have discussed. By using these techniques, data scientists and analysts can better understand and interpret complex data sets, ultimately leading to better outcomes and more accurate insights.

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