5 Best Ways to Add a Row to an Empty DataFrame in Python

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💡 Problem Formulation: When working with data in Python, it’s common to use pandas DataFrames to organize and manipulate data. Sometimes, we start with an empty DataFrame and need to add rows of data to it over time. This article explains how to add a row to an empty DataFrame in Python using pandas, including specific input examples and the resulting output DataFrame.

Method 1: Using loc Indexer

This method utilizes the loc indexer to assign a list of values to a new row index in an empty DataFrame. The loc indexer extends the DataFrame if the index does not exist. This method is best when you know the index value you want to assign to the new row.

Here’s an example:

import pandas as pd

# Create an empty DataFrame with predefined columns
df = pd.DataFrame(columns=['A', 'B', 'C'])

# Add a new row by index using 'loc'
df.loc[0] = [1, 2, 3]

print(df)

Output:

   A  B  C
0  1  2  3

This snippet shows how to add a single row to an empty DataFrame by specifying the row index and a list of values corresponding to each column. The loc indexer effectively increases the size of the DataFrame and inserts the new values.

Method 2: Using the append() Method

The append() method allows you to add a new row to the DataFrame. You pass a new row in the form of a dictionary, with the keys matching the DataFrame’s column names. This method does not mutate the original DataFrame, returning a new DataFrame instead.

Here’s an example:

import pandas as pd

# Create an empty DataFrame with predefined columns
df = pd.DataFrame(columns=['A', 'B', 'C'])

# Add a new row using a dictionary and 'append'
df = df.append({'A': 1, 'B': 2, 'C': 3}, ignore_index=True)

print(df)

Output:

   A  B  C
0  1  2  3

The code above demonstrates appending a row to an empty DataFrame using a dictionary that represents the new row. ignore_index=True is necessary to avoid key errors and to ensure the index is maintained correctly.

Method 3: Using DataFrame.loc with a Series

Another way to add a row to an empty DataFrame is by passing a pandas Series object with the loc indexer. This is similar to the first method but provides an alternative through Series, which may be more convenient if the data is already in that format.

Here’s an example:

import pandas as pd

# Create an empty DataFrame with predefined columns
df = pd.DataFrame(columns=['A', 'B', 'C'])

# Create a Series with data to be added
new_row = pd.Series([4, 5, 6], index=['A', 'B', 'C'])

# Add the Series as a new row using 'loc'
df.loc[len(df)] = new_row

print(df)

Output:

   A  B  C
0  4  5  6

By using a Series with an index matching the DataFrame’s columns, we can add a new row with ease. The length of the DataFrame, len(df), determines the index of the new row.

Method 4: Using pd.concat() with a DataFrame

In this method, we use the pd.concat() function to concatenate the original empty DataFrame with another DataFrame that contains the new row(s). This method is powerful when you have multiple rows to add and they are already organized in another DataFrame.

Here’s an example:

import pandas as pd

# Create an empty DataFrame with predefined columns
df = pd.DataFrame(columns=['A', 'B', 'C'])

# Add a new row by creating another DataFrame and concatenating
new_row_df = pd.DataFrame([[7, 8, 9]], columns=['A', 'B', 'C'])
df = pd.concat([df, new_row_df], ignore_index=True)

print(df)

Output:

   A  B  C
0  7  8  9

In this example, we create a new DataFrame with the row we want to add and concatenate it with the original DataFrame. The ignore_index=True option is used to reindex the DataFrame properly.

Bonus One-Liner Method 5: Using at() or iat()

For a quick, one-line addition of a row to an empty DataFrame, you can use the at() method when dealing with a single cell or iat() with positional indexing. This is a direct and fast way to insert single values if needed.

Here’s an example:

import pandas as pd

# Create an empty DataFrame with predefined columns
df = pd.DataFrame(columns=['A', 'B', 'C'])

# Add a new row using 'at'
df.at[0, 'A'] = 10
df.at[0, 'B'] = 11
df.at[0, 'C'] = 12

print(df)

Output:

    A   B   C
0  10  11  12

This code snippet quickly adds a single row by directly assigning values to specific positions in the DataFrame. Each at call sets the value for a particular cell.

Summary/Discussion

  • Method 1: Using loc Indexer. Straightforward for index-based operations. May be less efficient for adding multiple rows.
  • Method 2: Using the append() method. Clear syntax and allows adding dictionaries directly. It creates a new object, which can be less efficient for large DataFrames.
  • Method 3: Using DataFrame.loc with a Series. Offers a smooth workflow when dealing with Series objects. Involves an extra step of series creation.
  • Method 4: Using pd.concat() with a DataFrame. Ideal for adding multiple rows at once. It can be overkill for single-row additions.
  • Method 5: Using at() or iat(). Quick and precise for setting individual cell values. Not suitable for adding full rows efficiently.