How to deal with SettingWithCopyWarning in Pandas

The "SettingWithCopyWarning" in pandas is raised when you try to modify a copy of a DataFrame or Series rather than the original. This can happen, for example, when you try to set the value of a single element or a slice of a DataFrame or Series, but the operation is performed on a view of the original data rather than the data itself.

There are a few ways to deal with this warning, depending on what you're trying to do. One way is to use the .loc or .iloc accessors, which allow you to select and modify a subset of the DataFrame or Series using the labels or indices, respectively. Here's an example:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Use .loc to modify a single element
df.loc[0, 'A'] = 10

# Use .loc to modify a slice of the DataFrame
df.loc[:, 'B'] = df['B'] * 2

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Another way is to use the .copy() method to create a new DataFrame that is not tied to the original data. Here's an example:

import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})

# Make a copy of the DataFrame
df_copy = df.copy()

# Modify the copy
df_copy['A'] = df_copy['A'] * 2

If the warning is raised even though you are working on a copy of the dataframe and you are still sure that it's a copy and you want to change the data, you can temporarily suppress the warning using the pd.options.mode.chained_assignment attribute like this:

import pandas as pd
pd.options.mode.chained_assignment = None
#Do your modification
df_copy['A'] = df_copy['A'] * 2
pd.options.mode.chained_assignment = 'warn' # set back to default value

It's also worth noting that The warning can also be raised because the DataFrame or Series you're trying to modify is a chained assignment from a boolean indexing, which is an advanced and powerful feature of Pandas, but it can lead to unexpected behavior. This can be solved by using the .loc accessor, or by making a copy of the DataFrame or Series before modifying it.

It's a good practice to understand the source of the warning and modify the approach accordingly, to avoid unexpected behavior in the data modification.