How to print two columns from a dataframe in python
In this article, we will discuss different ways to select multiple columns of dataframe by name in pandas. Table of Contents Suppose we have a dataframe df with following contents, We want to select multiple columns from this dataframe. Let’s see how to do that, To select a multiple columns of a dataframe, pass a list of column names to the [] (subscript operator)
of the dataframe i.e. Advertisements col_names = ['City', 'Age'] # Select multiple columns of dataframe by names in list multiple_columns = df[col_names] print(multiple_columns) Output City Age 0 Sydney 34 1 Delhi 31 2 London 16 3 Delhi 41 When we passed a list containing two column names in the [] operator of the dataframe, it returned a subset of dataframe as a different dataframe object with only those two columns i.e. ‘City’ and ‘Age’. Also the returned subset is a view of the dataframe. Any modifications done in this, will be reflected in the original dataframe. Let’s checkout an example, where we will select two dataframes column name ‘City’ and ‘Age’ from the dataframe, import pandas as pd # List of Tuples empoyees = [('Jack', 34, 'Sydney', 5) , ('Riti', 31, 'Delhi' , 7) , ('Aadi', 16, 'London', 11) , ('Mark', 41, 'Delhi' , 12)] # Create a DataFrame object df = pd.DataFrame( empoyees, columns=['Name', 'Age', 'City', 'Experience']) print("Contents of the Dataframe : ") print(df) col_names = ['City', 'Age'] # Select multiple columns of dataframe by names in list multiple_columns = df[col_names] print("Selected Columns of Dataframe : ") print(multiple_columns) Output: Contents of the Dataframe : Name Age City Experience 0 Jack 34 Sydney 5 1 Riti 31 Delhi 7 2 Aadi 16 London 11 3 Mark 41 Delhi 12 Selected Columns of Dataframe : City Age 0 Sydney 34 1 Delhi 31 2 London 16 3 Delhi 41 Select multiple columns of pandas dataframe using loc[]We can also select multiple columns of the dataframe using its loc[] attribute. But before that let’s have a little overview of the loc[] attribute, Overview of dataframe.loc[]In pandas, dataframe provides an attribute loc[] to select rows or columns of a dataframe based on names. It’s syntax is as follows, df.loc[rows_section : column_section] Arguments:
Returns:
Example of selecting multiple columns of dataframe by name using loc[]We can select the multiple columns of dataframe, by passing a list of column names in the columns_section of loc[] and in rows_section pass the value “:”, to select all value of these columns. For example, col_names = ['City', 'Age'] # Select multiple columns of dataframe by name multiple_columns = df.loc[: , col_names] Output: City Age 0 Sydney 34 1 Delhi 31 2 London 16 3 Delhi 41 In the rows_section we passed the “:”. Whereas, in the columns_section we passed the list of column names only. Therefore it returned all the values of those columns from the dataframe as a different dataframe object. But this subset dataframe is a view of the original dataframe. Any modifications done in this, will be reflected in the original dataframe. Complete example with to select a multiple columns of dataframe using loc[] is as follows, import pandas as pd # List of Tuples empoyees = [('Jack', 34, 'Sydney', 5) , ('Riti', 31, 'Delhi' , 7) , ('Aadi', 16, 'London', 11) , ('Mark', 41, 'Delhi' , 12)] # Create a DataFrame object df = pd.DataFrame( empoyees, columns=['Name', 'Age', 'City', 'Experience']) print("Contents of the Dataframe : ") print(df) col_names = ['City', 'Age'] # Select multiple columns of dataframe by name multiple_columns = df.loc[: , col_names] print("Selected Columns of Dataframe : ") print(multiple_columns) Output: Contents of the Dataframe : Name Age City Experience 0 Jack 34 Sydney 5 1 Riti 31 Delhi 7 2 Aadi 16 London 11 3 Mark 41 Delhi 12 Selected Columns of Dataframe : City Age 0 Sydney 34 1 Delhi 31 2 London 16 3 Delhi 41 Summary: We learned about two different ways to select multiple columns of dataframe. How do I extract two columns from a data frame?There are three basic methods you can use to select multiple columns of a pandas DataFrame:. Method 1: Select Columns by Index df_new = df. iloc[:, [0,1,3]]. Method 2: Select Columns in Index Range df_new = df. iloc[:, 0:3]. Method 3: Select Columns by Name df_new = df[['col1', 'col2']]. How do I print columns from a data frame?For example, if our dataframe is called df we just type print(df. columns) to get all the columns of the Pandas dataframe.
How do I display specific columns in a data frame?If you have a DataFrame and would like to access or select a specific few rows/columns from that DataFrame, you can use square brackets or other advanced methods such as loc and iloc .
How do I copy two columns of a DataFrame in Python?Use pandas.. data = {"col1":[1, 2, 3], "col2":[4, 5, 6], "col3":[7, 8, 9]}. df = pd. DataFrame(data). new_df = selected_columns. copy(). print(new_df). |