Pandas DataFrame.set_flags

Pandas DataFrame.set_flags

The DataFrame.set_flags method in pandas is used to set user-modifiable flags on a DataFrame. These flags allow you to control behaviors like copying data or allowing duplicate labels in the DataFrame.

Syntax

The syntax for DataFrame.set_flags is:

DataFrame.set_flags(*, copy=False, allows_duplicate_labels=None)

Here, DataFrame refers to the pandas DataFrame whose flags are being set.

Parameters

Returns

A DataFrame with updated flags.

Examples

Setting the allows_duplicate_labels Flag

Use set_flags to control whether the DataFrame allows duplicate labels.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35]
}
df = pd.DataFrame(data)

# Set the allows_duplicate_labels flag to False
print("Setting allows_duplicate_labels to False:")
df.set_flags(allows_duplicate_labels=False)
print(df.flags)

Output

Setting allows_duplicate_labels to False:
<class 'pandas.core.flags.AllFlags'>
allows_duplicate_labels: False

Returning a New DataFrame with Updated Flags

Set the copy parameter to True to return a new DataFrame with the updated flags instead of modifying the original DataFrame in place.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35]
}
df = pd.DataFrame(data)

# Return a new DataFrame with allows_duplicate_labels set to True
print("Creating a new DataFrame with allows_duplicate_labels=True:")
df_new = df.set_flags(copy=True, allows_duplicate_labels=True)
print(df_new.flags)
print("\nOriginal DataFrame flags:")
print(df.flags)

Output

Creating a new DataFrame with allows_duplicate_labels=True:
<class 'pandas.core.flags.AllFlags'>
allows_duplicate_labels: True

Original DataFrame flags:
<class 'pandas.core.flags.AllFlags'>
allows_duplicate_labels: None

Resetting Flags to Default

You can reset flags to their default state by setting allows_duplicate_labels=None.

Python Program

import pandas as pd

# Create a DataFrame
data = {
    'Name': ['Arjun', 'Ram', 'Priya'],
    'Age': [25, 30, 35]
}
df = pd.DataFrame(data)

# Set allows_duplicate_labels to False and then reset it to default
print("Setting allows_duplicate_labels to False and then resetting to default:")
df.set_flags(allows_duplicate_labels=False)
print("After setting to False:")
print(df.flags)
df.set_flags(allows_duplicate_labels=None)
print("\nAfter resetting to default:")
print(df.flags)

Output

Setting allows_duplicate_labels to False and then resetting to default:
After setting to False:
<class 'pandas.core.flags.AllFlags'>
allows_duplicate_labels: False

After resetting to default:
<class 'pandas.core.flags.AllFlags'>
allows_duplicate_labels: None

Summary

In this tutorial, we explored the DataFrame.set_flags method in pandas. Key takeaways include:

• Using set_flags to modify user-modifiable flags in a DataFrame.
• Controlling whether duplicate labels are allowed using allows_duplicate_labels.
• Returning a new DataFrame with updated flags using copy=True.

The DataFrame.set_flags method is a useful tool for managing specific DataFrame behaviors in pandas.