Understanding the NaN Value Issue in Pandas Column Selection

If you have ever tried to extract specific columns from a larger Pandas DataFrame to create a smaller sub-dataframe, only to end up with a table full of NaN (null) values, you are not alone. This is a common pitfall when working with Python data analysis, especially before performing aggregation operations like .groupby().

In this article, we will examine why this happens, look at the incorrect syntax that causes index misalignment, and explore the cleanest, most efficient ways to subset your Pandas DataFrame properly.

The Problem: Incorrect Usage of pd.DataFrame()

Consider a scenario where you have a main DataFrame named train containing multiple columns, including 'Address' and 'Price'. A developer might try to create a new sub-dataframe like this:

# Incorrect approach that leads to NaN values
Address_df = pd.DataFrame(train.Price, train.Address)
Address_df_2 = Address_df.groupby('Address').mean()
display(Address_df_2)

Why does this return NaN values?

The signature for creating a new DataFrame manually is pd.DataFrame(data, index, columns, ...). When you write pd.DataFrame(train.Price, train.Address), Pandas interprets:

  • First parameter (data): train.Price (the data values).
  • Second parameter (index): train.Address (used to define row labels/indices for the new DataFrame).

Because Pandas attempts to reindex train.Price using the values of train.Address as row indices, a mismatch occurs between the original integer index of train.Price and the string address values in train.Address. As a result, Pandas cannot align the indices and fills the entire Price column with NaN.

The Solutions: How to Correctly Select Columns in Pandas

Solution 1: Use Double Brackets for Subsetting (Recommended)

The simplest and most idiomatic way to extract a subset of columns from an existing DataFrame is by passing a list of column names inside double square brackets [['col1', 'col2']]:

import pandas as pd

# Select 'Address' and 'Price' correctly
Address_df = train[['Address', 'Price']]

# Group by 'Address' and calculate the mean price
Address_df_2 = Address_df.groupby('Address').mean()

display(Address_df_2)

Solution 2: GroupBy directly from the original DataFrame

You do not need to create an intermediate DataFrame just to group your data. You can perform the selection and aggregation in a single line directly on your original DataFrame train:

# Calculate the mean price per address in one clean step
result = train.groupby('Address')['Price'].mean().reset_index()

display(result)

Using .reset_index() converts the grouped 'Address' index back into a standard column, giving you a clean two-column DataFrame with non-null values.

Solution 3: Pass a Dictionary if Instantiating a New DataFrame

If you explicitly want to construct a new DataFrame from existing Pandas Series, pass them as a key-value dictionary to define column names explicitly:

# Explicitly map column names using a dictionary
Address_df = pd.DataFrame({
    'Address': train['Address'],
    'Price': train['Price']
})

Address_df_2 = Address_df.groupby('Address').mean()
display(Address_df_2)

Summary

Passing multiple Series directly as positional arguments to pd.DataFrame() causes Pandas to treat the second argument as custom row indices, leading to index misalignment and NaN outputs. To avoid this, always use list subsetting df[['col1', 'col2']] or execute your .groupby() operation directly on the source DataFrame.