Pandas Dataframe filter() method is “used to filter the DataFame rows and columns.” The returned DataFrame contains only rows and columns specified by the function.
Syntax
DataFrame.filter(self: ~FrameOrSeries, items=None, like: Union[str, NoneType] = None,
regex: Union[str, NoneType] = None, axis=None)Parameters
items: list-like
Keep labels from the axis that are in items.
like: str
Keep labels from the axis for which “like in label == True”.
regex: str (regular expression)
Keep labels from the axis for which re.search(regex, label) == True.
axis: {0 or ‘index’, 1 or ‘columns’, None}, the default value is None.
The axis to filter on is expressed either as the index (int) or axis name (str).
By default, this is an info axis, ‘index’ for Series, and ‘columns’ for DataFrame.
Return value
It returns the same type as the input object.
Example 1: How to Use Pandas.DataFrame.filter() Method
import pandas as pd
data = {
'A': [1, 2, 3],
'B': [4, 5, 6],
'C': [7, 8, 9]
}
df = pd.DataFrame(data)
# Filter columns using items
filtered_columns = df.filter(items=['A', 'C'])
print("Filtered columns using items:\n", filtered_columns)
# Filter columns using like
filtered_columns_like = df.filter(like='A')
print("\nFiltered columns using like:\n", filtered_columns_like)
# Filter columns using regex
filtered_columns_regex = df.filter(regex='[BC]')
print("\nFiltered columns using regex:\n", filtered_columns_regex)
# Filter rows using items
filtered_rows = df.filter(items=[0, 2], axis=0)
print("\nFiltered rows using items:\n", filtered_rows)Output
Filtered columns using items: A C 0 1 7 1 2 8 2 3 9 Filtered columns using like: A 0 1 1 2 2 3 Filtered columns using regex: B C 0 4 7 1 5 8 2 6 9 Filtered rows using items: A B C 0 1 4 7 2 3 6 9
Example 2: Pandas.DataFrame.filter() Columns by Labels
import pandas as pd
# Create a DataFrame
df = pd.DataFrame({
'A': [1, 2, 3],
'B': [4, 5, 6],
'C': [7, 8, 9]
})
# Use filter() to select columns 'A' and 'C'
filtered_df = df.filter(items=['A', 'C'])
print(filtered_df)
Output
A C
0 1 7
1 2 8
2 3 9
Example 3: Pandas.DataFrame.filter() Rows by Index
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3],
'B': [4, 5, 6],
'C': [7, 8, 9]
}, index=['row1', 'row2', 'row3'])
filtered_df = df.loc[['row1', 'row3']]
print(filtered_df)
Output
A B C
row1 1 4 7
row3 3 6 9
That’s it.
Krunal Lathiya is a seasoned Computer Science expert with over eight years in the tech industry. He boasts deep knowledge in Data Science and Machine Learning. Versed in Python, JavaScript, PHP, R, and Golang. Skilled in frameworks like Angular and React and platforms such as Node.js. His expertise spans both front-end and back-end development. His proficiency in the Python language stands as a testament to his versatility and commitment to the craft.
