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If you wish to retrieve the first column of a Pandas DataFrame, you can use the iloc property. This is an integer-based indexing method which allows you to select a column by its integer index. For example, df.iloc[:,0] will return the first column of the DataFrame. You can also use the column names as an indexer, for example, df[‘column_name’] will return the requested column. As an alternative, you can use the dot notation, by calling df.column_name. This returns the same result as the previous indexing method. These methods can be combined with slicing to select more than one column. Additionally, you can use loc and select the first column by its label. To do this, use df.loc[:, ‘column_name’]. This last method is useful when the column names are not integers.
You can use the following syntax to get the first column of a pandas DataFrame:
df.iloc[:, 0]
The result is a pandas Series. If you’d like the result to be a pandas DataFrame instead, you can use the following syntax:
df.iloc[:, :1]
The following examples show how to use this syntax in practice.
Example 1: Get First Column of Pandas DataFrame (Return a Series)
The following code shows how to get the first column of a pandas DataFrame:
import pandas as pd #create DataFrame df = pd.DataFrame({'points': [25, 12, 15, 14, 19, 23, 25, 29], 'assists': [5, 7, 7, 9, 12, 9, 9, 4], 'rebounds': [11, 8, 10, 6, 6, 5, 9, 12]}) #view DataFrame print(df) points assists rebounds 0 25 5 11 1 12 7 8 2 15 7 10 3 14 9 6 4 19 12 6 5 23 9 5 6 25 9 9 7 29 4 12 #get first column first_col = df.iloc[:, 0] #view first column print(first_col) 0 25 1 12 2 15 3 14 4 19 5 23 6 25 7 29 Name: points, dtype: int64
The result is a pandas Series:
#check type of first_col print(type(first_col)) <class 'pandas.core.series.Series'>
Example 2: Get First Column of Pandas DataFrame (Return a DataFrame)
The following code shows how to get the first column of a pandas DataFrame and return a DataFrame as a result:
#get first column (and return a DataFrame) first_col = df.iloc[:, :1] #view first column print(first_col) points 0 25 1 12 2 15 3 14 4 19 5 23 6 25 7 29 #check type of first_col print(type(first_col)) <class 'pandas.core.frame.DataFrame'>