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Conditional Selection in Pandas
Understanding Conditional Selection:
Conditional selection in Pandas allows you to filter data based on specific conditions. This is an essential feature for data analysis, enabling the extraction of relevant data subsets.
Importance in Data Analysis:
Using conditional selection, analysts can focus on data that meets certain criteria, making it easier to perform targeted analysis and derive insights.
Syntax Overview:
The basic syntax involves using a boolean condition inside the DataFrame's indexing brackets. For example, df[df['column'] > value].
Example 1: Selecting Rows Based on a Single Condition
Filter DataFrame by a Single Column:
This example demonstrates how to filter a DataFrame to include only rows where a specific column meets a condition.
import pandas as pd
# Sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [24, 17, 35]}
df = pd.DataFrame(data)
# Select rows where Age is greater than 20
filtered_df = df[df['Age'] > 20]
print(filtered_df)
Explanation:
In this example, the condition df['Age'] > 20 returns a boolean Series used to filter the DataFrame.
Console Output:
Name Age 0 Alice 24 2 Charlie 35
Example 2: Using Multiple Conditions
Combining Conditions with Logical Operators:
You can use logical operators like & (and), | (or) to combine multiple conditions for more complex filtering.
import pandas as pd
# Sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [24, 17, 35], 'City': ['NY', 'LA', 'NY']}
df = pd.DataFrame(data)
# Select rows where Age is greater than 20 and City is NY
filtered_df = df[(df['Age'] > 20) & (df['City'] == 'NY')]
print(filtered_df)
Explanation:
Here, the DataFrame is filtered by combining two conditions: Age greater than 20 and City equal to 'NY'. Both conditions must be true for a row to be included.
Console Output:
Name Age City 0 Alice 24 NY
Example 3: Using the isin() Method
Filtering with the isin() Method:
The isin() method is useful for filtering data based on whether a column's values are in a list of values.
import pandas as pd
# Sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [24, 17, 35], 'City': ['NY', 'LA', 'NY']}
df = pd.DataFrame(data)
# Select rows where City is either NY or LA
filtered_df = df[df['City'].isin(['NY', 'LA'])]
print(filtered_df)
Explanation:
The isin() method checks if each value in the 'City' column is in the list ['NY', 'LA'], filtering the DataFrame accordingly.
Console Output:
Name Age City 0 Alice 24 NY 1 Bob 17 LA 2 Charlie 35 NY
Example 4: Using the query() Method
Simplifying Conditions with query():
The query() method allows you to filter a DataFrame using a string expression, making the syntax cleaner and more readable.
import pandas as pd
# Sample DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [24, 17, 35], 'City': ['NY', 'LA', 'NY']}
df = pd.DataFrame(data)
# Select rows where Age is less than 30 using query()
filtered_df = df.query('Age < 30')
print(filtered_df)
Explanation:
The query() method uses a string expression to filter rows, in this case selecting rows where Age is less than 30.
Console Output:
Name Age City 0 Alice 24 NY 1 Bob 17 LA
Example 5: Conditional Selection with NaN Values
Handling NaN Values in Conditional Selection:
When filtering, you might encounter NaN values. The isnull() and notnull() methods help handle these cases.
import pandas as pd
import numpy as np
# Sample DataFrame with NaN values
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [24, np.nan, 35], 'City': ['NY', 'LA', np.nan]}
df = pd.DataFrame(data)
# Select rows where Age is not NaN
filtered_df = df[df['Age'].notnull()]
print(filtered_df)
Explanation:
The notnull() method is used to filter out NaN values from the 'Age' column, ensuring only complete records are selected.
Console Output:
Name Age City 0 Alice 24 NY 2 Charlie 35 NaN
