💡 Problem Formulation: In data manipulation with Pandas, a common task is to filter columns from a DataFrame based on the sum of their values. This requirement arises when we need to exclude or include columns which meet certain criteria related to their summed data. For example, from an input DataFrame of multiple numerical columns, we might want to retain only those columns where the sum of their values exceeds a specified threshold.
Method 1: Boolean Indexing with df.columns
Boolean indexing with df.columns utilizes a boolean mask to select columns whose sum meets a specific condition. By summing each column and comparing the results against the threshold, we generate a boolean mask, which is then applied to the df.columns to filter the DataFrame.
Here’s an example:
import pandas as pd
# Creating a sample DataFrame
df = pd.DataFrame({
'A': [1, 2, 3],
'B': [4, 5, 6],
'C': [7, 8, 9]
})
# Defining the threshold
threshold = 10
# Filtering columns with a sum greater than the threshold
filtered_columns = df.columns[df.sum() > threshold]
filtered_df = df[filtered_columns]
print(filtered_df)
Output:
B C 0 4 7 1 5 8 2 6 9
In this code snippet, we create a DataFrame and define a threshold. We then filter out columns where the sum of values is greater than the defined threshold. The variable filtered_columns holds the column names that meet the condition, and filtered_df is the resulting DataFrame with the filtered columns.
Method 2: Using the filter Function with lambda
This method involves the use of the filter function along with a lambda function to achieve the filtration. The lambda function checks the sum of each column and returns only the columns that fulfill the condition, thereby filtering the DataFrame.
Here’s an example:
import pandas as pd
# Creating another DataFrame
df = pd.DataFrame({
'D': [2, 3, 4],
'E': [5, -1, 0],
'F': [-7, 7, 14]
})
# Filtering columns using the filter function
filtered_df = df.loc[:, lambda df: df.sum() > 10]
print(filtered_df)
Output:
F 0 -7 1 7 2 14
The lambda function inside the loc method is used to apply a column-wise filtering criterion. Here, it filters out the columns where the sum is greater than 10. The resulting DataFrame filtered_df only includes such columns.
Method 3: The drop Method with Column Selection
The drop method can be used to remove columns that don’t meet the sum condition. First we select columns which have a sum less than the threshold and then use df.drop to exclude them, effectively filtering the DataFrame.
Here’s an example:
import pandas as pd
# Sample DataFrame
df = pd.DataFrame({
'G': [10, 20, 30],
'H': [40, 0, -10],
'I': [5, 5, 5]
})
# Dropping columns where the sum is less than 45
columns_to_drop = df.columns[df.sum() < 45]
filtered_df = df.drop(columns=columns_to_drop)
print(filtered_df)
Output:
G H 0 10 40 1 20 0 2 30 -10
In this snippet, we obtain the list of columns to drop where the sum is less than 45. By using df.drop with this list, we are left with columns in filtered_df that have a sum greater than or equal to this threshold.
Method 4: The query Method
The query method does not readily apply to column filtering but can be used in combination with transposing the DataFrame. This method leverages the flexible string expressions within query to filter columns indirectly after the transpose operation, then transposes back to the original form.
Here’s an example:
import pandas as pd
# Another DataFrame
df = pd.DataFrame({
'J': [100, 200, -50],
'K': [1, 1, 1],
'L': [0, 50, 100]
})
# Using query after transposing the DataFrame
filtered_df = df.T.query('sum() > 150').T
print(filtered_df)
Output:
J L 0 100 0 1 200 50 2 -50 100
This method transposes the DataFrame, applies a filter on the transposed rows (original columns), and transposes back to the original structure. The use of query allows for a clear and concise filtering expression.
Bonus One-Liner Method 5: Sum and Boolean Broadcasting
A one-liner solution combines the sum operation with boolean broadcasting to filter columns. This concise method performs the sum and condition evaluation all at once, resulting in a compact line of code for filtering.
Here’s an example:
import pandas as pd
# DataFrame for one-liner
df = pd.DataFrame({
'M': [0, 50, 100],
'N': [150, 0, 150],
'O': [-100, 0, 100]
})
# One-liner filtering
filtered_df = df.loc[:, df.sum() > 100]
print(filtered_df)
Output:
N O 0 150 -100 1 0 0 2 150 100
The filtering expression df.sum() > 100 is used directly within the indexing operator of the DataFrame to select the appropriate columns, making this a very succinct approach to column filtering based on sum.
Summary/Discussion
Method 1: Boolean Indexing. Strengths include readability and simplicity. Weaknesses may be the creation of intermediate variables for column selection.
Method 2: filter Function with lambda. It gives a clear expression of intent and leverages DataFrame’s own functionality. The downside is the slight complexity introduced by the lambda for newcomers.
Method 3: The drop Method. It’s a direct approach that uses pandas’ built-in method. However, negating the condition for column sums to find columns to drop might be counterintuitive for some users.
Method 4: The query Method. It’s powerful, especially with complex conditions and reads nicely. On the flip side, this method relies on transposing the DataFrame twice, which can be inefficient for very large datasets.
Method 5: Sum and Boolean Broadcasting. This method is extremely concise but may be less readable to someone unfamiliar with pandas’ broadcasting rules.