5 Best Ways to Convert Python Dict to DataFrame Row

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💡 Problem Formulation:

Python developers often use dictionaries to carry data because of their flexibility and ease of use. However, when working with pandas DataFrames, it is frequently necessary to convert a dictionary into a DataFrame row. This can be challenging for those unfamiliar with pandas transformations. The problem we’re solving is to demonstrate multiple ways to add a dictionary as a new row in an existing DataFrame, where the keys correspond to the column names and the values to the row data. The input is a Python dictionary, and the desired output is a modified DataFrame.

Method 1: Using DataFrame.append()

This method involves using the append() function of the pandas DataFrame to add a dictionary as a new row. The dictionary is wrapped in a list to ensure it is treated as a single row.

Here’s an example:

import pandas as pd

# Initial DataFrame
df = pd.DataFrame(columns=['A', 'B', 'C'])

# Dictionary to add as a new row
data_dict = {'A': 1, 'B': 2, 'C': 3}

# Adding the dictionary as a new row
df = df.append([data_dict], ignore_index=True)

print(df)

Output:

   A  B  C
0  1  2  3

This method updates the DataFrame with the new row from the dictionary, incrementing its index. Although very handy, df.append() can be less performance-efficient for large DataFrames due to the copying of data.

Method 2: Using pandas.concat()

Concatenation is a powerful pandas operation that can be applied to combine multiple objects. By creating a DataFrame from the dictionary and concatenating it to the original DataFrame, a row can be effectively added.

Here’s an example:

import pandas as pd

# Initial DataFrame
df = pd.DataFrame(columns=['A', 'B', 'C'])

# Dictionary to add as a new row
data_dict = {'A': 4, 'B': 5, 'C': 6}

# Creating a DataFrame from the dictionary and concatenating
new_row = pd.DataFrame([data_dict])
df = pd.concat([df, new_row], ignore_index=True)

print(df)

Output:

   A  B  C
0  4  5  6

This method creates a separate DataFrame from the dictionary and then concatenates it to the existing DataFrame. While it is more performant than append() for larger DataFrames, it can still be inefficient if done in a loop.

Method 3: Using DataFrame.loc[]

The loc[] accessor of pandas can be utilized to insert a dictionary into a DataFrame, treating the keys as column names and assigning the values to the new row index.

Here’s an example:

import pandas as pd

# Initial DataFrame
df = pd.DataFrame(columns=['A', 'B', 'C'])

# Dictionary to add as a new row
data_dict = {'A': 7, 'B': 8, 'C': 9}

# Adding the dictionary as a new row using loc
df.loc[len(df)] = data_dict

print(df)

Output:

   A  B  C
0  7  8  9

This method uses the loc[] property to directly add a row, circumventing the need to create an intermediate DataFrame and is well-suited for instances where we are adding rows one at a time.

Method 4: Using Dictionary Unpacking Within DataFrame.append()

Another approach with append() is to unpack the dictionary directly as the row to append. This method leverages the use of the double-star syntax to unpack the dictionary’s items as parameter keywords.

Here’s an example:

import pandas as pd

# Initial DataFrame
df = pd.DataFrame(columns=['A', 'B', 'C'])

# Dictionary to add as a new row
data_dict = {'A': 10, 'B': 11, 'C': 12}

# Adding the dictionary as a new row using unpacking
df = df.append(data_dict, ignore_index=True)

print(df)

Output:

    A   B   C
0  10  11  12

This approach minimizes code by directly unpacking the dictionary into the append function. It is more performant than the initial append method but shares the same drawbacks when used in large-scale operations.

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

For a quick one-liner solution to add multiple dictionaries as rows, list comprehension combined with append() can be used. This method is concise but should be used cautiously with large data volumes.

Here’s an example:

import pandas as pd

# Initial DataFrame
df = pd.DataFrame(columns=['A', 'B', 'C'])

# List of dictionaries to add as new rows
data_dicts = [{'A': 13, 'B': 14, 'C': 15}, {'A': 16, 'B': 17, 'C': 18}]

# Adding multiple dictionaries as new rows using a list comprehension
df = df.append(data_dicts, ignore_index=True)

print(df)

Output:

    A   B   C
0  13  14  15
1  16  17  18

This one-liner comprehends through a list of dictionaries, appending each to the DataFrame in a single operation. While elegant for short lists, it inherits the efficiency concerns of the append function.

Summary/Discussion

  • Method 1: DataFrame.append(). Easy to understand and clear syntax. However, it is not the best choice performance-wise for larger datasets.
  • Method 2: pandas.concat(). Better suited for performance when dealing with large data. However, it can also be resource-intensive when used repeatedly in loops.
  • Method 3: DataFrame.loc[]. Direct and efficient for adding single rows. It becomes cumbersome when adding multiple rows at once.
  • Method 4: Dictionary Unpacking. Offers cleaner code and is efficient for appending single rows. Like others, it is not the most efficient for multiple large-scale appends.
  • Method 5: List Comprehension with append(). Most concise for adding multiple rows. It should be used with caution for large datasets.