Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels. It can be thought of as a dict-like container for Series objects. This is the primary data structure of the Pandas.
Pandas DataFrame.columns attribute return the column labels of the given Dataframe.
Syntax: DataFrame.columns Parameter : None Returns : column names
Use DataFrame.columns attribute to return the column labels of the given Dataframe
Python3
# importing pandas as pdimport pandas as pd# Creating the DataFramedf = pd.DataFrame({'Weight':[45, 88, 56, 15, 71], 'Name':['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'], 'Age':[14, 25, 55, 8, 21]})# Create the indexindex_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']# Set the indexdf.index = index_# Print the DataFrameprint(df) |
Output :
Now we will use DataFrame.columns attribute to return the column labels of the given dataframe.
Python3
# return the column labelsresult = df.columns# Print the resultprint(result) |
Output :
As we can see in the output, the DataFrame.columns attribute has successfully returned all of the column labels of the given dataframe.
Use DataFrame.columns attribute to return the column labels of the given Dataframe
Python3
# importing pandas as pdimport pandas as pd# Creating the DataFramedf = pd.DataFrame({"A":[12, 4, 5, None, 1], "B":[7, 2, 54, 3, None], "C":[20, 16, 11, 3, 8], "D":[14, 3, None, 2, 6]})# Create the indexindex_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']# Set the indexdf.index = index_# Print the DataFrameprint(df) |
Output :
Now we will use DataFrame.columns attribute to return the column labels of the given dataframe.
Python3
# return the column labelsresult = df.columns# Print the resultprint(result) |
Output :
As we can see in the output, the DataFrame.columns attribute has successfully returned all of the column labels of the given dataframe.
