To exclude columns in a Pandas DataFrame, you can use the drop() method. Below are a few examples of how to exclude columns using drop():
Methods:
1. Exclude Columns by Name:
import pandas as pd
# Sample DataFrame
data = {
"A": [1, 2, 3],
"B": [4, 5, 6],
"C": [7, 8, 9]
}
df = pd.DataFrame(data)
# Exclude column 'B'
df_excluded = df.drop(columns=["B"])
print(df_excluded)
Output:
A C 0 1 7 1 2 8 2 3 9
2. Exclude Columns by Index:
# Exclude the second column (index 1) df_excluded = df.drop(df.columns[1], axis=1) print(df_excluded)
Output:
A C 0 1 7 1 2 8 2 3 9
3. Exclude Multiple Columns:
# Exclude columns 'A' and 'C' df_excluded = df.drop(columns=["A", "C"]) print(df_excluded)
Output:
B 0 4 1 5 2 6
4. Using filter() to Keep Desired Columns:
# Keep only columns 'A' and 'C' df_excluded = df.filter(["A", "C"]) print(df_excluded)
Output:
A C 0 1 7 1 2 8 2 3 9
5. Exclude Columns with Specific Conditions:
# Exclude columns that start with 'B'
df_excluded = df.loc[:, ~df.columns.str.startswith("B")]
print(df_excluded)
Output:
A C 0 1 7 1 2 8 2 3 9
Summary:
In Pandas, excluding columns from a DataFrame can be achieved using the versatile drop() method or alternative approaches. The drop() method allows you to remove columns by specifying their names in the columns parameter, e.g., df.drop(columns=[“col1”]). For multiple columns, simply pass a list of column names. If you prefer to drop columns by their index positions, you can use df.drop(df.columns[index], axis=1). Another approach is to select the desired columns and exclude others by using the filter() method, e.g., df.filter([“col1”, “col2”]).
You can also exclude columns based on specific conditions. For example, to remove columns whose names start with a specific pattern, use the loc function with logical indexing, such as df.loc[:, ~df.columns.str.startswith(“pattern”)]. These methods provide flexibility in handling columns dynamically, whether you’re working with names, indices, or conditions. Overall, Pandas makes it straightforward to exclude columns from a DataFrame, helping you customize data for analysis or visualization.