Keeping Your Data Clean: Deleting Rows in Pandas DataFrames

Libraries:

• pandas: This is a Python library specifically designed for data analysis and manipulation. It offers powerful tools for working with DataFrames, which are essentially two-dimensional tables with labeled axes.

Process:

1. Import pandas: You'll need to import the pandas library at the beginning of your Python script. Here's how to do it:

import pandas as pd

2. Delete Rows: Here's how you can delete the first three rows:

   • Using iloc: The iloc property allows you to access rows and columns of a DataFrame by position. To delete the first three rows, you can use slicing notation with iloc:

   df = df.iloc[3:]  # This selects rows from index 3 (fourth row) onwards
   

   Explanation:

   • df: This refers to your existing DataFrame.
   • .iloc[3:]: This part selects rows. The 3 specifies the starting index (fourth row) and the colon : indicates all rows after that (excluding the first three).

Important Note:

• The iloc method modifies the original DataFrame by default. If you want to create a new DataFrame without modifying the original one, you can assign the result of df.iloc[3:] to a new variable.

Example:

import pandas as pd

# Create a sample DataFrame
data = {'col1': [1, 2, 3, 4, 5], 'col2': ['A', 'B', 'C', 'D', 'E']}
df = pd.DataFrame(data)

# Print the original DataFrame
print(df)

# Delete the first three rows
df_modified = df.iloc[3:]

# Print the DataFrame after deleting rows
print(df_modified)

This code will first create a DataFrame with two columns, then delete the first three rows using iloc, and finally print both the original and modified DataFrames.

import pandas as pd

# Create a sample DataFrame
data = {'col1': [1, 2, 3, 4, 5], 'col2': ['A', 'B', 'C', 'D', 'E']}
df = pd.DataFrame(data)

# Print the original DataFrame
print("Original DataFrame:")
print(df)

# Delete the first three rows (Option 1: Modifying the original DataFrame)
df = df.iloc[3:]  # This modifies the original DataFrame

# Print the DataFrame after deleting rows (using the modified DataFrame)
print("\nDataFrame after deleting first 3 rows (modifying original):")
print(df)

# Option 2: Creating a new DataFrame without modifying the original
df_new = df.iloc[3:]  # Create a new DataFrame without modifying the original

# Print the DataFrame after deleting rows (using the new DataFrame)
print("\nDataFrame after deleting first 3 rows (new DataFrame):")
print(df_new)

1. Import pandas: We import the pandas library as pd.
2. Create DataFrame: We create a sample DataFrame df with two columns (col1 and col2).
3. Print Original DataFrame: We print the original DataFrame to see the initial data.
4. Delete Rows (Option 1):
5. Delete Rows (Option 2):
6. Print New DataFrame: We print the newly created df_new to demonstrate how you can work with the modified data without affecting the original DataFrame.

This code provides two options: modifying the original DataFrame and creating a new one. You can choose the approach that best suits your needs.

Using .drop() with boolean indexing:

import pandas as pd

# Create a sample DataFrame
data = {'col1': [1, 2, 3, 4, 5], 'col2': ['A', 'B', 'C', 'D', 'E']}
df = pd.DataFrame(data)

# Delete the first three rows using boolean indexing
df_filtered = df[~df.index.isin(range(3))]  # Select rows where index is not in range(3)

# Print the DataFrame after deleting rows
print(df_filtered)

• We create a boolean mask using ~df.index.isin(range(3)). This creates a Series where True indicates rows to keep (not in the first three indices).
• We use this mask with .drop() to filter the DataFrame and keep only the desired rows.

Using .head() (for illustrative purposes):

# This method only keeps the first N rows, not for deleting

df_kept = df.head(n=len(df) - 3)  # Keep all rows except the first three

Note:

• .head() is typically used to retrieve the first N rows, not for deleting. However, it can be helpful for illustrative purposes to understand how to keep a specific number of rows from the end.

Remember to choose the method that best suits your needs and coding style.