Python | Delete rows/columns from DataFrame using Pandas.drop()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric Python packages. Pandas is one of those packages which makes importing and analyzing data much easier.

Drop Function in Pandas

Pandas provide data analysts with a way to delete and filter data frames using dataframe.drop() method. Rows or columns can be removed using an index label or column name using this method.

Syntax:
DataFrame.drop(labels=None, axis=0, index=None, columns=None, level=None, inplace=False, errors=’raise’)

Parameters:
labels: String or list of strings referring row or column name.
axis: int or string value, 0 ‘index’ for Rows and 1 ‘columns’ for Columns.
index or columns: Single label or list. index or columns are an alternative to axis and cannot be used together. level: Used to specify level in case data frame is having multiple level index.
inplace: Makes changes in original Data Frame if True.
errors: Ignores error if any value from the list doesn’t exists and drops rest of the values when errors = ‘ignore’ Return type: Dataframe with dropped values

Dropping Rows by index label

In this code, A list of index labels is passed and the rows corresponding to those labels are dropped using .drop() method. To download the CSV used in the code, click here.

Python3

# importing pandas module
import pandas as pd

# making data frame from csv file
data = pd.read_csv("nba.csv", index_col="Name")
print(data.head(5))

Output: Data Frame before Dropping values

                         Team  Number Position   Age Height  Weight            College     Salary
Name                                                                  
Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0              Texas  7730337.0
Jae Crowder    Boston Celtics    99.0       SF  25.0    6-6   235.0          Marquette  6796117.0
John Holland   Boston Celtics    30.0       SG  27.0    6-5   205.0  Boston University        NaN
R.J. Hunter    Boston Celtics    28.0       SG  22.0    6-5   185.0      Georgia State  1148640.0
Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0                NaN  5000000.0

Applying the drop function.

Python3

# dropping passed values
data.drop(["Avery Bradley", "John Holland", "R.J. Hunter"], inplace = True)

# display
print(data)

Output: Data Frame after Dropping values

As shown in the output before, the new output doesn’t have the passed values. Those values were dropped and the changes were made in the original data frame since inplace was True.

                         Team  Number Position   Age Height  Weight   College     Salary
Name                                                                  
Jae Crowder    Boston Celtics    99.0       SF  25.0    6-6   235.0 Marquette  6796117.0
Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0       NaN  5000000.0 
Amir Johnson   Boston Celtics    90.0       PF  29.0    6-9   240.0       NaN 12000000.0 
Jordan Mickey  Boston Celtics    55.0       PF  21.0    6-8   235.0       LSU  1170960.0 
Kelly Olynyk   Boston Celtics    41.0        C  25.0    7-0   238.0   Gonzaga  2165160.0  

Dropping columns with column name

In this code, Passed columns are dropped using column names. axis parameter is kept 1 since 1 refers to columns.

Python3

# importing pandas module
import pandas as pd

# making data frame from csv file
data = pd.read_csv("nba.csv", index_col ="Name" )
print(data.head())

Output: Data Frame before Dropping Columns

                         Team  Number Position   Age Height  Weight            College     Salary
Name                                                                  
Avery Bradley  Boston Celtics     0.0       PG  25.0    6-2   180.0              Texas  7730337.0
Jae Crowder    Boston Celtics    99.0       SF  25.0    6-6   235.0          Marquette  6796117.0
John Holland   Boston Celtics    30.0       SG  27.0    6-5   205.0  Boston University        NaN
R.J. Hunter    Boston Celtics    28.0       SG  22.0    6-5   185.0      Georgia State  1148640.0
Jonas Jerebko  Boston Celtics     8.0       PF  29.0   6-10   231.0                NaN  5000000.0

Applying drop function.

Python3

# dropping passed columns
data.drop(["Team", "Weight"], axis = 1, inplace = True)

# display
print(data.head())

Output: Data Frame after Dropping Columns

As shown in the output images, the new output doesn’t have the passed columns. Those values were dropped since the axis was set equal to 1 and the changes were made in the original data frame since inplace was True.

               Number Position   Age Height            College     Salary
Name                                                                     
Avery Bradley     0.0       PG  25.0    6-2              Texas  7730337.0
Jae Crowder      99.0       SF  25.0    6-6          Marquette  6796117.0
John Holland     30.0       SG  27.0    6-5  Boston University        NaN
R.J. Hunter      28.0       SG  22.0    6-5      Georgia State  1148640.0
Jonas Jerebko     8.0       PF  29.0   6-10                NaN  5000000.0