<\/span><\/h2>\n<\/p>\n
Filtering a DataFrame based on column values can be easily achieved by using boolean indexing. Boolean indexing allows us to select rows based on a condition, such as values in a specific column. The basic syntax for filtering a DataFrame is as follows:<\/p>\n
df_filtered = df[df['column_name'] condition]<\/code><\/pre>\nLet’s break down the above code:<\/p>\n
<\/p>\n
<\/p>\ndf['column_name']<\/code> represents the column in the DataFrame that you want to filter.<\/li>\n<\/p>\n
condition<\/code> is the condition that the column values should satisfy for the row to be selected.<\/li>\n<\/p>\n
df_filtered<\/code> is the resulting DataFrame containing only rows that meet the specified condition.<\/li>\n\n<\/ul>\n
Here’s an example to demonstrate the filtering process:<\/p>\n
# Import pandas library
\nimport pandas as pd
\n
\n# Create a sample DataFrame
\ndata = {'Name': ['John', 'Jane', 'Mike', 'Sarah'],
\n 'Age': [25, 30, 35, 40],
\n 'City': ['New York', 'London', 'Paris', 'Tokyo']}
\ndf = pd.DataFrame(data)
\n
\n# Filter the DataFrame based on Age greater than 30
\ndf_filtered = df[df['Age'] > 30]
\n
\n# Print the filtered DataFrame
\nprint(df_filtered)
\n<\/code><\/pre>\nOutput:<\/p>\n
Name Age City
\n2 Mike 35 Paris
\n3 Sarah 40 Tokyo
\n<\/code><\/pre>\nIn the above example, we filtered the DataFrame df<\/code> based on the condition df['Age'] > 30<\/code>, which selected only the rows where the age was greater than 30.<\/p>\n