The comma-separated values ββ(CSV) file is a delimited text file that uses commas for individual values. Each line of the file is a data record in CSV. This format used for tabular data, rows, and columns, exactly like a spreadsheet. The CSV file stores data in rows and the values ββin each row are separated with a comma(separator), also known as a delimiter.
There are 2 ways to remove a column entirely from a CSV in python. Let us now focus on the techniques :
- With pandas library β drop() or pop()
- Without pandas library
Here, a simple CSV file is used i.e; input.csv
| id | day | month | year | item_quantity | Name |
| 1 | 12 | 3 | 2020 | 12 | Oliver |
| 2 | 13 | 3 | 2020 | 45 | Henry |
| 3 | 14 | 3 | 2020 | 8 | Benjamin |
| 4 | 15 | 3 | 2020 | 23 | John |
| 5 | 16 | 3 | 2020 | 31 | Camili |
| 6 | 17 | 3 | 2020 | 40 | Rheana |
| 7 | 18 | 3 | 2020 | 55 | Joseph |
| 8 | 19 | 3 | 2020 | 13 | Raj |
| 9 | 20 | 3 | 2020 | 29 | Elias |
| 10 | 21 | 3 | 2020 | 19 | Emily |
Method 1: Using pandas library
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 and makes importing and analyzing data much easier. Pandas consist of a drop function that is used in removing rows or columns from the CSV files. Pandas Pop() method is common in most of the data structures but the pop() method is a little different from the rest. In a stack, pop doesnβt require any parameters, it pops the last element every time. But the pandas pop method can take input of a column from a data frame and pop that directly.
Example 1: Using drop()
data.drop( labels=None, axis=0, index=None, columns=None, level=None, inplace=False,errors='raise')
- Import Pandas
- Read CSV File
- Use drop() function for removing or deleting rows or columns from the CSV files
- Print Data
Python3
# import pandas with shortcut 'pd'import pandas as pdΒ Β Β Β # read_csv function which is used to read the required CSV filedata = pd.read_csv('input.csv')Β Β # displayΒ print("Original 'input.csv' CSV Data: \n")print(data)Β Β # drop function which is used in removing or deleting rows or columns from the CSV filesdata.drop('year', inplace=True, axis=1)Β Β # displayΒ print("\nCSV Data after deleting the column 'year':\n")print(data) |
Output:
Example 2: Using pop()
We can use the panda pop () method to remove columns from CSV by naming the column as an argument.
- Import Pandas
- Read CSV File
- Use pop() function for removing or deleting rows or columns from the CSV files
- Print Data
Python3
# import pandas with shortcut 'pd'import pandas as pdΒ Β # read_csv function which is used to read the required CSV filedata = pd.read_csv('input.csv')Β Β # displayprint("Original 'input.csv' CSV Data: \n")print(data)Β Β # pop function which is used in removing or deleting columns from the CSV filesdata.pop('year')Β Β # displayprint("\nCSV Data after deleting the column 'year':\n")print(data) |
Output:
Method 2: Using CSV library
Example 3: Using CSV read and write
- Open Input CSV file as source
- Read Source CSV File
- Open Output CSV File as a result
- Put source CSV data in result CSV using indexes
Python3
# import csvimport csvΒ Β # open input CSV file as source# open output CSV file as resultwith open("input.csv", "r") as source:Β Β Β Β reader = csv.reader(source)Β Β Β Β Β Β Β Β Β Β with open("output.csv", "w") as result:Β Β Β Β Β Β Β Β writer = csv.writer(result)Β Β Β Β Β Β Β Β for r in reader:Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β Β # Use CSV Index to remove a column from CSVΒ Β Β Β Β Β Β Β Β Β Β Β #r[3] = r['year']Β Β Β Β Β Β Β Β Β Β Β Β writer.writerow((r[0], r[1], r[2], r[4], r[5])) |
Output:
