Understanding the Problem and Setting Up Our Example

To tackle this problem, we need to understand what’s going on with SettingWithCopyWarning and how it affects our workflow. The warning occurs when we’re trying to set values in a DataFrame using a method that doesn’t guarantee we have access to the original data.

For example, if we try to do something like:

my_df_nona_1 = my_df.dropna()
my_df_nona_1.loc[:, 'dob'] = pd.to_datetime(my_df_nona_1.loc[:, 'dob'], format='%d.%m.%Y')

We’re essentially doing two separate operations: dropping NaN values and then converting the date column. The problem here is that dropna() doesn’t return a copy of the original DataFrame, but rather a new one without any missing values. Then when we assign a new value to 'dob', it’s possible we’re modifying a copy, not the original data.

Let’s start by setting up our example:

import numpy as np
import pandas as pd

name = ['John', 'Melinda', 'Greg', 'Amanda']
dob = ['20.12.2001', '11.03.1991', '31.12.1999', np.nan]
my_df = pd.DataFrame({'name': name, 'dob': dob})

print(my_df)
# Output:
#     name        dob
# 0   John  20.12.2001
# 1  Melinda  11.03.1991
# 2    Greg  31.12.1999
# 3    Amanda         NaN

This is our initial DataFrame with missing values in the 'dob' column.

Understanding SettingWithCopyWarning

Now, let’s dive into what’s happening behind the scenes when we’re using methods like .dropna() and .loc[]. These methods can return either a new DataFrame object or modify the original one depending on how they’re called.

Here’s an example of what happens when you call .dropna(): it creates a copy of the original DataFrame, so if you assign it to another variable:

my_df_nona = my_df.dropna()

Then my_df remains unchanged. However, the problem arises when we try to modify this new DataFrame. Since it’s not a direct assignment to an existing column but rather a modification of a copy of that column (or even a different operation entirely), pandas warns us about modifying a potentially modified copy.

Similarly, .loc[] returns a view on a slice of the original DataFrame and doesn’t create a new one unless you explicitly assign it back:

my_df_nona = my_df.loc[:, 'dob']

Here we’re seeing why the warning occurs; when we modify my_df_nona, it may not affect my_df because it’s only a view on the slice of data.

Now let’s use our example with .dropna() to see how this affects us:

# Drop NaN values using .dropna()
my_df_nona = my_df.dropna()

print(my_df_nona)
# Output:
#     name        dob
# 0   John  20.12.2001
# 1  Melinda  11.03.1991

# Convert date column using to_datetime()
my_df_nona['dob'] = pd.to_datetime(my_df_nona['dob'], format='%d.%m.%Y')

print(my_df_nona)

In this case, the resulting DataFrame is a new one without any missing values and with the date column converted correctly.

Next we’ll see an example using .loc[]:

# Drop NaN values using .dropna() then convert date column using to_datetime()
my_df_nona = my_df.dropna().loc[:, 'dob']
my_df_nona = pd.to_datetime(my_df_nona, format='%d.%m.%Y')

print(my_df_nona)

Here we see how assigning a new value directly back onto my_df_nona creates the warning.

The Correct Approach: Using DataFrame.assign()

Now let’s explore how you can avoid this warning and achieve your desired result. One way to do it is by using the .assign() method:

# Chain .dropna() with .assign() for to_datetime()
df = my_df.dropna().assign(dob = lambda x: pd.to_datetime(x['dob'], format='%d.%m.%Y'))

print(df)

Here, we create a new column 'dob' within the same DataFrame after dropping any rows containing NaN values.

Using .assign() ensures that we’re not modifying an existing column but rather creating a new one. This avoids the possibility of using a potentially modified copy and removes the warning altogether!

In summary, the issue with chaining operations on DataFrames is mainly due to how certain methods like .dropna() and .loc[] behave when used in this manner.

Using .assign() provides an elegant solution by allowing us to create new columns within existing dataframes without having to deal with potential issues arising from modifying copies of that column or using a different operation.

Best Practices for Workflow

• Use .copy(): For most use cases, .copy() is the safest way to ensure you’re working on a copy rather than the original DataFrame. This can be especially useful when dealing with potentially large DataFrames.

my_df_nona = my_df.dropna().copy()

-   **Be Mindful of How You Assign**: Pay close attention to how you assign values back into your original DataFrame after modifying it using methods like `.dropna()` or `.loc[]`.
    ```markdown
# Avoid this approach due to the warning:
my_df_nona = my_df.dropna().loc[:, 'dob']
my_df_nona = pd.to_datetime(my_df_nona, format='%d.%m.%Y')

• Use .assign(): When you need to create new columns within an existing DataFrame after performing operations like dropping NaN values or modifying specific data points, use the .assign() method.

Recommended approach:

df = my_df.dropna().assign(dob=lambda x: pd.to_datetime(x[‘dob’], format=’%d.%m.%Y’))

By being aware of these best practices and using them when working with DataFrames in pandas, you can avoid common pitfalls like the `SettingWithCopyWarning` and write more efficient code.

Last modified on 2023-09-14