5 Best Ways to Create a DataFrame Row from a List in Python

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💡 Problem Formulation: Imagine you have a list of data in Python, such as [1, 'Alice', 4.5], and you want to add it as a new row to an existing DataFrame within the pandas library. You’d like to convert the list into a DataFrame row, preserving the order and data type of elements in the list. The desired output is an updated DataFrame that includes the new row at the bottom.

Method 1: Using DataFrame.append()

The DataFrame.append() method in pandas allows you to add a new row to the end of a DataFrame. The row to be appended can be specified as a dictionary, where the keys correspond to the DataFrame’s columns.

Here’s an example:

import pandas as pd

df = pd.DataFrame(columns=['Id', 'Name', 'Score'])
row_list = [2, 'Bob', 3.7]
row_to_append = pd.Series(row_list, index=df.columns)
df = df.append(row_to_append, ignore_index=True)
print(df)

Output:

  Id  Name  Score
0  2   Bob   3.7

This code snippet starts by importing the pandas library. We create an empty DataFrame with specified column names. We then convert the list into a Series, specifying the dataframe’s columns as the index. The append() function is used to add the Series as a new row to the DataFrame.

Method 2: Using DataFrame.loc[]

The DataFrame.loc[] method enables you to access a group of rows and columns by labels. You can use it to add a new row by specifying a new index that is currently not used in the DataFrame.

Here’s an example:

import pandas as pd

df = pd.DataFrame(columns=['Id', 'Name', 'Score'])
row_list = [3, 'Charlie', 5.0]
new_index = len(df)
df.loc[new_index] = row_list
print(df)

Output:

  Id     Name  Score
0  3  Charlie    5.0

The snippet begins by creating an empty DataFrame. We then calculate the length of the DataFrame, which is used as the new row index. The list is added directly as a new row using df.loc with this new index.

Method 3: Using DataFrame.concat()

With DataFrame.concat(), you can concatenate along a particular axis. This method is well-suited for combining two DataFrames. To add a list as a row, you first need to convert it to a DataFrame and then concatenate.

Here’s an example:

import pandas as pd

df = pd.DataFrame(columns=['Id', 'Name', 'Score'])
row_list = [[4, 'David', 2.3]]
new_row = pd.DataFrame(row_list, columns=df.columns)
df = pd.concat([df, new_row], ignore_index=True)
print(df)

Output:

  Id   Name  Score
0  4  David    2.3

This code snippet first creates an empty DataFrame. The given list is wrapped inside another list to represent a 2D array, which is then converted to a DataFrame. Finally, the pd.concat() method is used to add this new DataFrame as a row.

Method 4: Using DataFrame.append() with a Dictionary

This is a variation of Method 1 where DataFrame.append() is used with a dictionary. The list is zipped with the columns of the DataFrame to create a dictionary, which is then appended as a row.

Here’s an example:

import pandas as pd

df = pd.DataFrame(columns=['Id', 'Name', 'Score'])
row_list = [5, 'Eve', 4.8]
row_dict = dict(zip(df.columns, row_list))
df = df.append(row_dict, ignore_index=True)
print(df)

Output:

  Id Name Score
0  5  Eve   4.8

This code snippet creates a dictionary from the DataFrame’s columns and the list using the zip() function. The dictionary is then appended to the DataFrame using the append() method.

Bonus One-Liner Method 5: Using DataFrame.append() in a List Comprehension

This one-liner method utilizes list comprehension to append multiple rows stored as a list of lists into the DataFrame using append().

Here’s an example:

import pandas as pd

df = pd.DataFrame(columns=['Id', 'Name', 'Score'])
rows_list = [[6, 'Frank', 3.1], [7, 'Grace', 4.6]]
df = pd.concat([df, pd.DataFrame(rows, columns=df.columns)] for rows in rows_list)
print(df)

Output:

  Id   Name  Score
0  6  Frank    3.1
1  7  Grace    4.6

A list of rows is created as a list of lists. Within a list comprehension, each of the internal lists is converted to a DataFrame and concatenated with the original DataFrame using pd.concat().

Summary/Discussion

  • Method 1: Using DataFrame.append() with Series. Strengths: Straightforward for single rows; preserves data types. Weaknesses: Appending multiple rows is less efficient.
  • Method 2: Using DataFrame.loc[]. Strengths: Easy to read; good for conditionally adding rows. Weaknesses: Requires management of index.
  • Method 3: Using DataFrame.concat(). Strengths: Ideal for adding multiple rows or DataFrames. Weaknesses: Slightly more complex syntax for single rows.
  • Method 4: Using DataFrame.append() with a Dictionary. Strengths: Intuitive for single rows; mirrors DataFrame structure. Weaknesses: Appending many rows might be inefficient.
  • Method 5: Bonus One-Liner using List Comprehension. Strengths: Elegant for adding many rows. Weaknesses: Potentially difficult to debug for complex scenarios.