Python Pandas Drop Dataframe

1.2K Asked by AbhishekShukla in Python , Asked on Apr 14, 2021

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df = pd.read_csv('C:LoadProfilesCSVWillBaySchl 2013_2014 KW.csv')
print(df)
This will return the head/tail and:[34944 rows x 3 columns]
pos0 = 0
pos1 = 1
pos2 = 2
colname = df.columns[pos0]
print(colname)
This will return: Meter ID (I want to drop this column/dataframe)
colname = df.columns[pos1]
print(colname)
This will return: Date / Time (I want this to be the pd data frame index)
colname = df.columns[pos2]
print(colname)
This will return: KW(ch: 1  set:0) (This is the data that I want to rename "kW" and do some wrangling...)
If I try this code below:
df = pd.DataFrame.drop(['Meter ID'], axis=1)
print(df)
Python will return the error:TypeError: drop() missing 1 required positional argument: 'labels'
If I try this code below:
df = pd.DataFrame.drop(columns=['Meter ID'])
print(df)
Python will return the error: TypeError: drop() got an unexpected keyword argument 'columns'
Any help is greatly appreciated...

Answered by Abhishek Shukla

To solve “drop() got an unexpected keyword argument 'columns' “ error you are required to delete the single column, we can use:

df = pd.DataFrame.drop('Meter ID', axis=1)

For deleting more than 1 column:

df = pd.DataFrame.drop(['Meter ID', 'abc'], axis=1)

And for deleting the entire df you can use:

del df

or

df = None

Your Answer

Answer (1)

Ranjana

To drop a DataFrame in Python using Pandas, you have a couple of options depending on whether you want to drop rows or columns. Here’s how you can do it:

Drop Rows or Columns

Drop Rows by Index or Condition:

Use the drop() method with the index or a condition to drop specific rows.

import pandas as pd

  # Example DataFramedf = pd.DataFrame({    'A': [1, 2, 3, 4],    'B': ['a', 'b', 'c', 'd']})# Drop rows by index (e.g., index 0 and 2)df_dropped = df.drop([0, 2])print("Original DataFrame:")print(df)print("

DataFrame after dropping rows:")print(df_dropped)

Output:

Original DataFrame:

     A  B0  1  a1  2  b2  3  c3  4  d

DataFrame after dropping rows:

     A  B1  2  b3  4  d

Alternatively, you can drop rows based on a condition:

# Drop rows where column 'A' is greater than 2

  df_dropped = df[df['A'] > 2]

  print("DataFrame after dropping rows based on condition:")print(df_dropped)

Output:

DataFrame after dropping rows based on condition:

     A  B2  3  c3  4  d

Drop Columns by Label:

Use the drop() method with the column labels to drop specific columns.

  # Drop column 'B'df_dropped = df.drop(columns=['B'])print("Original DataFrame:")print(df)print("

DataFrame after dropping column:")print(df_dropped) Output:

Original DataFrame:

     A  B0  1  a1  2  b2  3  c3  4  d

DataFrame after dropping column:

     A0  11  22  33  4

In-Place Dropping

By default, the drop() method returns a new DataFrame with the specified rows or columns dropped without modifying the original DataFrame. If you want to modify the original DataFrame, you can use the inplace=True parameter.

  # Drop rows with index 0 and 2 in placedf.drop([0, 2], inplace=True)print("DataFrame after dropping rows in place:")print(df)

Conclusion

Dropping rows or columns in Pandas allows you to manipulate DataFrames efficiently based on your data analysis needs. Ensure to use these methods appropriately based on whether you need to drop rows, columns, or perform in-place operations.

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