Pandas Tutorial

• Introduction to Pandas in Python
• How to Install Python Pandas on Windows and Linux?
• How To Use Jupyter Notebook - An Ultimate Guide

Creating Objects

• Python | Pandas DataFrame
• Creating a Pandas DataFrame
• Python | Pandas Series
• Creating a Pandas Series

Viewing Data

• View the top rows of the frame
• View the bottom rows of the frame
• View basic statistical details
• Convert the pandas DataFrame to numpy Array
• Convert the pandas Series to numpy Array
• Convert series or dataframe object to Numpy-array using .as_matrix().

Selection

• Dealing with Rows and Columns in Pandas DataFrame
• How to select multiple columns in a pandas dataframe
• Python | Pandas Extracting rows using .loc[]
• Python | Extracting rows using Pandas .iloc[]
• Indexing and Selecting Data with Pandas
• Boolean Indexing in Pandas
• Label and Integer based slicing technique using DataFrame.ix[ ]

Manipulating Data

• Adding new column to existing DataFrame in Pandas
• Python | Delete rows/columns from DataFrame
• Truncate a DataFrame before and after some index value
• Truncate a Series before and after some index value
• Iterating over rows and columns in Pandas DataFrame
• Working with Missing Data in Pandas
• Sorts a data frame in Pandas

Grouping Data

• Pandas GroupBy
• Grouping Rows in pandas
• Combining multiple columns in Pandas groupby with dictionary

Merging, Joining and Concatenating

• Python | Pandas Merging, Joining, and Concatenating
• Concatenate Strings
• Append rows to Dataframe
• Concatenate two or more series
• Append a single or a collection of indices
• Combine two series into one
• Add a row at top in pandas DataFrame
• Join all elements in list present in a series
• Join two text columns into a single column in Pandas

Working with Date and Time

• Python | Working with date and time using Pandas
• Timestamp using Pandas
• Current Time using Pandas
• Convert timestamp to ISO Format
• Get datetime object using Pandas
• Replace the member values of the given Timestamp
• Convert string Date time into Python Date time object using Pandas
• Get a fixed frequency DatetimeIndex using Pandas

Working With Text Data

• Python | Pandas Working With Text Data
• Convert String into lower, upper or camel case
• Replace Text Value
• Replace Text Value using series.replace()
• Removing Whitespaces
• Move dates forward a given number of valid dates using Pandas

Working with CSV and Excel files

• Read csv using pandas
• Saving a Pandas Dataframe as a CSV
• Loading Excel spreadsheet as pandas DataFrame
• Creating a dataframe using Excel files
• Working with Pandas and XlsxWriter

Operations

• Apply a function on the possible series
• Apply function to every row in a Pandas DataFrame
• Apply a function on each element of the series
• Aggregation data across one or more column
• Mean of the values for the requested axis
• Mean of the underlying data in the Series
• Mean absolute deviation of the values for the requested axis
• Mean absolute deviation of the values for the Series
• Unbiased standard error of the mean
• Find the Series containing counts of unique values
• Find the Series containing counts of unique values using Index.value_counts()

Visualization

• Pandas Built-in Data Visualization
• Data analysis and Visualization with Python
• Box plot visualization with Pandas and Seaborn

Applications and Projects

• How to Do a vLookup in Python using pandas
• Convert CSV to HTML Table in Python
• KDE Plot Visualization with Pandas and Seaborn
• Analyzing selling price of used cars using Python
• Add CSS to the Jupyter Notebook using Pandas

Python | Delete rows/columns from DataFrame

Deleting rows or columns from a pandas DataFrame is a common operation. Here's a step-by-step tutorial on how to do it:

1. Setup:

Firstly, ensure you have the required library installed:

pip install pandas

2. Import the necessary library:

import pandas as pd

3. Create a Sample DataFrame:

For the tutorial, let's use this sample DataFrame:

data = {
    'A': [1, 2, 3, 4, 5],
    'B': [10, 20, 30, 40, 50],
    'C': ['p', 'q', 'r', 's', 't']
}

df = pd.DataFrame(data)
print(df)

4. Delete Rows:

a. Using drop method:

To delete, for example, the row with index 2:

df = df.drop(2)
print(df)

To delete multiple rows, say with index 1 and 3:

df = df.drop([1, 3])
print(df)

b. Using Boolean Indexing:

Let's say we want to remove rows where column 'A' is less than 3:

df = df[df['A'] >= 3]
print(df)

5. Delete Columns:

a. Using drop method:

To delete column 'A':

df = df.drop(columns=['A'])
print(df)

To delete multiple columns, say 'A' and 'C':

df = df.drop(columns=['A', 'C'])
print(df)

b. Using del keyword:

To delete column 'B':

del df['B']
print(df)

6. Using inplace Parameter:

For all the deletions we did above, we assigned the result back to df. Instead, you can use the inplace parameter of the drop method to modify the DataFrame in place:

df.drop(columns=['A'], inplace=True)

7. Resetting Index:

After deleting rows, you may want to reset the index of your DataFrame:

df.reset_index(drop=True, inplace=True)
print(df)

Here, the drop=True argument ensures that the old index is discarded and not added as a new column.

Summary:

Deleting rows or columns in a pandas DataFrame is straightforward. The drop method is versatile and allows for easy removal of rows/columns. Remember to use the inplace parameter if you don't wish to assign the result back to the DataFrame. Always handle your data with care to ensure you don't accidentally remove critical information.

1. Pandas DataFrame drop rows by index:

   • Use the drop() method to remove rows based on their index.
   • Example:

     # Drop rows by index
     df = df.drop([0, 1])
     

2. Delete columns from Pandas DataFrame:

   • Use the drop() method with axis=1 to remove columns by name.
   • Example:

     # Delete columns by name
     df = df.drop(['Column1', 'Column2'], axis=1)
     

3. Remove rows with specific condition in Pandas DataFrame:

   • Filter rows based on a condition and create a new DataFrame.
   • Example:

     # Remove rows with specific condition
     df = df[df['Column'] != 'value']
     

4. Python DataFrame dropna method:

   • Use the dropna() method to remove rows with missing values.
   • Example:

     # Drop rows with missing values
     df = df.dropna()
     

5. Drop duplicate rows in Pandas DataFrame:

   • Use drop_duplicates() to remove duplicate rows.
   • Example:

     # Drop duplicate rows
     df = df.drop_duplicates()
     

6. How to delete a row by condition in Pandas:

   • Use a boolean condition to filter rows and create a new DataFrame.
   • Example:

     # Delete row by condition
     df = df[df['Column'] != 'value']
     

7. Drop columns with certain values in Pandas DataFrame:

   • Filter columns based on values and create a new DataFrame.
   • Example:

     # Drop columns with certain values
     df = df.loc[:, ~df.columns.isin(['Column1', 'Column2'])]
     

8. Pandas DataFrame drop columns by name:

   • Use the drop() method with axis=1 to remove columns by name.
   • Example:

     # Drop columns by name
     df = df.drop(['Column1', 'Column2'], axis=1)
     

9. Delete empty rows in Python DataFrame:

   • Use the dropna() method to remove rows with missing values.
   • Example:

     # Delete empty rows
     df = df.dropna()
     

10. Drop rows based on multiple conditions in Pandas:

    • Combine multiple conditions using logical operators for row removal.
    • Example:

      # Drop rows based on multiple conditions
      df = df[(df['Column1'] != 'value') & (df['Column2'] > 10)]