How do I select and store columns greater than a number in pandas

When working with large datasets, there may be situations where we need to filter and store columns based on specific conditions. For instance, we may want to select all columns where values exceed a certain threshold and store them in a new DataFrame for further analysis.

This article will explore various methods to accomplish this task, from basic to advanced, and discuss how to store the results efficiently.

Prerequisites

Before we dive into the methods, make sure we have Pandas installed in our environment. We can install it using pip if we haven't already:

pip install pandas

Filtering Columns Based on Values in Pandas

We'll start by importing the necessary library and creating a sample DataFrame.

import pandas as pd

# Create a sample DataFrame
data = {
    'A': [10, 20, 30, 40, 50],
    'B': [15, 25, 35, 45, 55],
    'C': [5, 10, 15, 20, 25],
    'D': [50, 60, 70, 80, 90]
}

df = pd.dataframe(data=data)
print(data)

Output

Method 1: Selecting Columns Using Boolean Indexing

Boolean indexing is a powerful technique to filter data in Pandas. To select columns where the values are greater than a certain number, we can apply a condition directly to the DataFrame.

Example: Selecting Columns Greater Than 30

Here, we see NaN values for entries that don't meet the condition. The DataFrame retains its original shape, but only the values greater than 30 are shown.

Method 2: Dropping Columns That Don't Meet the Condition

If we want to keep only the columns where all values are greater than a certain number, we can use the all() method in combination with Boolean indexing.

Example: Dropping Columns Where All Values Are Not Greater Than 30

In this case, only column D is retained because all its values are greater than 30.

# Dropping columns where not all values are greater than 30
df_all_greater_than_30 = df.loc[:, (df > 30).all()]
print(df_all_greater_than_30)

Output

Method 3: Selecting Columns Based on a Specific Row

You might want to filter columns based on the values in a specific row. For instance, selecting columns where the first row's values are greater than a number.

Example: Selecting Columns Where the First Row's Value Is Greater Than 10

Here, columns B and D are selected because the values in the first row (index 0) are greater than 10.

# Selecting columns based on the first row's values
df_row_based = df.loc[:, df.iloc[0] > 10]
print(df_row_based)

Output

Method 4: Selecting and Storing Columns Using apply()

We can also use the apply() function to apply a condition across each column and select columns based on this condition.

Example: Selecting Columns Where the Mean Value Is Greater Than 30

Here, columns B and D are selected because their mean values are greater than 30.

# Applying a condition using apply()
df_mean_greater_than_30 = df.loc[:, df.apply(lambda col: col.mean() > 30)]
print(df_mean_greater_than_30)

Output

Method 5: Storing the Selected Columns

Once we've filtered the DataFrame, we might want to store the selected columns for further analysis or export them to a file. We can do this by simply assigning the filtered DataFrame to a new variable or writing it to a CSV file using to_csv() method.

This command will save the filtered DataFrame to a CSV file named filtered_columns.csv.

# Storing in a New DataFrame
filtered_df = df[df > 30]

# Writing to a CSV File
filtered_df.to_csv('filtered_columns.csv', index=False)

Output

Advanced Method: Selecting Pandas Columns with Complex Conditions

Sometimes, we may need to filter columns based on more complex conditions, such as multiple thresholds or custom functions.

Example: Selecting Columns Based on a Custom Function

In this example, we define a custom function that selects columns where at least one value is greater than 20 and all values are less than 60. Columns A, B, and C meet this condition.

# Custom function to filter columns
def custom_condition(col):
    return (col > 20).any() and (col < 60).all()

# Applying the custom function
df_custom_filtered = df.loc[:, df.apply(custom_condition)]
print(df_custom_filtered)

Output

Conclusion:

Filtering and selecting columns based on conditions is a common task in data analysis, and Pandas provides multiple methods to accomplish this. Whether we need to select columns based on simple numerical thresholds or more complex criteria, Pandas' flexibility allows us to tailor our approach to our specific needs. After selecting the desired columns, we can easily store them for further processing or export them for external use.

By mastering these techniques, we can efficiently handle and analyze large datasets, focusing on the data that matters most.