Easy Ways To Append Data To Pandas Dataframes

Pandas is a powerful tool that allows users to store and manipulate data in a structured way, similar to an Excel spreadsheet or SQL table. It is a Python package designed for data manipulation and analysis, offering various operations and data structures to efficiently handle large datasets. One common task in data manipulation is adding rows to a Pandas DataFrame, which can be achieved through methods like loc[]], append(), and concat(). This process involves inserting new records or observations into the existing dataset, increasing its size and potentially improving the insights gained from analysis. In this discussion, we will explore the different methods for adding rows to a Pandas DataFrame, considering their advantages and use cases.

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Using the append() method

Pandas is a powerful tool that allows users to store and manipulate data in a structured way, similar to an Excel spreadsheet or SQL table. A Pandas DataFrame is a two-dimensional data structure with labelled axes (rows and columns).

The append() method is one of the methods used to add rows to a Pandas DataFrame. It allows users to add one or more rows to an existing Pandas DataFrame. This method is called on a Pandas DataFrame object, which means you must have one DataFrame already declared.

The append() function appends a DataFrame-like object to the end of the current DataFrame and returns a new DataFrame object. No changes are made to the original DataFrame. For example, if you have two DataFrames, df1 and df2, with columns "a" and "b", you can append df2 to the end of df1 using the code:

Python

Df1 = df1.append(df2, ignore_index=True)

This will create a new DataFrame, df1, with the rows of df2 appended to the end. The "ignore_index=True" argument resets the index of the new DataFrame, which is useful when using a default index to ensure the sequence is not broken.

It is important to note that as of Pandas version 2.0, the append() method is no longer in use, and the concat() function is recommended instead. The concat() function does not modify the original DataFrame but creates and returns a new DataFrame.

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Using the loc[] indexer

The .loc [] indexer is a powerful tool in Pandas for label-based indexing and data manipulation tasks such as selection, filtering, and conditional modifications. It uses the labels of rows or columns to access data, and these labels can be anything, including numbers or timestamps.

The .loc[] indexer is particularly useful when you have clearly defined labels that carry meaning. For example, you can use it to select a single row or column by specifying its label. Here's an example of selecting a single row:

Python

Df = pd.DataFrame({'Weight': [45, 88, 56, 15, 71],

Name': ['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'],

Age': [14, 25, 55, 8, 21]})

Set the index

Df.index = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']

Select a single row by label

Result = df.loc['Row_2']

Print(result)

This code will output the following:

Weight 88

Name Andrea

Age 25

Name: Row_2, dtype: object

You can also use .loc[] to select multiple rows or columns by passing a list of labels. For example, to select multiple rows:

Python

Select multiple rows by label

Result = df.loc[['Row_2', 'Row_4']]

Print(result)

The output will be:

Weight Name Age

Row_2 88 Andrea 25

Row_4 15 Robin 8

Similarly, you can select multiple columns:

Python

Select multiple columns by label

Result = df.loc[:, ['Weight', 'Age']]

Print(result)

Output:

Weight Age

Row_1 45 14

Row_2 88 25

Row_3 56 55

Row_4 15 8

Row_5 71 21

The .loc[] indexer can also be used for conditional filtering. For example, you can select rows where a specific column meets a certain condition:

Python

Import pandas library

Import pandas as pd

Creating the DataFrame

Df = pd.DataFrame({"A": [12, 4, 5, None, 1],

"B": [7, 2, 54, 3, None],

"C": [20, 16, 11, 3, 8],

"D": [14, 3, None, 2, 6]})

Create the index

Index_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']

Set the index

Df.index = index_

Select rows where column 'A' is greater than 5

Selected_rows = df.loc[df['A'] > 5]

Print("Rows where column 'A' is greater than 5:")

Print(selected_rows)

Output:

Rows where column 'A' is greater than 5:

A B C D

Row_1 12 7 20 14

Row_2 4 2 16 3

The .loc[] indexer is also useful for adding or updating single rows in a DataFrame. When adding a row, Pandas will check if the index label already exists. If it does, the existing row will be overwritten; otherwise, a new row will be appended with the provided index label. This is especially handy when you need to add specific, labeled data, such as student records.

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Using the concat() method

The `concat() method in Pandas is used to concatenate two or more Pandas DataFrame objects. Unlike the `append()` method, `concat()` is not a DataFrame method and belongs to the Pandas library instead. This method requires both elements to be of type `pd.DataFrame`, which means you will have to convert new records before concatenation.

To add a single row to a Pandas DataFrame using `concat()`, you first need to create a new DataFrame with a single row containing the data you want to add. Then, you can concatenate the new DataFrame with the original one using the `concat()` method. For example:

Python

Import pandas as pd

Create the original DataFrame

Data = {'name': ['John', 'Doe'], 'age': [35, 40]}

Df = pd.DataFrame(data)

Create a new row to append to the DataFrame

New_row = pd.DataFrame({'name': ['Jane'], 'age': [28]})

Append the new row to the original DataFrame

Df = pd.concat([df, new_row], ignore_index=True)

The `ignore_index=True` parameter is used to reset the index of the resulting DataFrame. If this parameter is not set to True, the resulting DataFrame will have the index values of the original DataFrame and the new row.

`concat()` is versatile and can concatenate DataFrames along both rows and columns. It can be used for more complex concatenation scenarios, such as when the columns in the DataFrames being combined do not match. It also allows concatenating multiple DataFrames in a single call.

It is important to note that concatenating dataframes is relatively expensive compared to appending to a list of lists. Therefore, it is not recommended to build DataFrames by adding single rows in a for loop. Instead, build a list of rows and make a DataFrame in a single `concat()` call.

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Adding multiple rows

There are three methods to add rows to a Pandas DataFrame: the append() method, loc [] indexer, and the concat() method.

Using the append() method

The append() method allows you to add one or more rows to an existing Pandas DataFrame. You can use this method to add multiple rows by saving each row that you want to add into a list of lists, creating a new DataFrame from this list, and then appending the new DataFrame to the original one. Here is an example:

Python

A list of 3 new employees

New_employees = [

{"First Name": "Joseph", "Last Name": "Dune", "Email": "[email protected]"},

{"First Name": "Jackie", "Last Name": "Slash", "Email": "[email protected]"},

{"First Name": "Ginni", "Last Name": "Mars", "Email": "[email protected]"}

]

Add all three new employees using append()

Data = data.append(new_employees, ignore_index=True)

Using the loc [] indexer

The loc [] indexer allows you to select data based on the labels assigned to the rows and columns of your DataFrame. You can also use it to add multiple rows to a Pandas DataFrame. Here is an example:

Python

A list of 3 new employees

New_employees = [

{"First Name": "Joseph", "Last Name": "Dune", "Email": "[email protected]"},

{"First Name": "Jackie", "Last Name": "Slash", "Email": "[email protected]"},

{"First Name": "Ginni", "Last Name": "Mars", "Email": "[email protected]"}

]

Add all three new employees using loc[]

For emp in new_employees:

Data.loc [len(data)] = emp

Using the concat() function

The concat() function is often used to add multiple rows to a Pandas DataFrame. It takes a list of DataFrames that you want to concatenate and joins them based on their indices or specified keys, aligning them vertically (row-wise) or horizontally (column-wise). Here is an example:

Python

A list of 3 new employees

NewRows = [

{"Roll":11,"Maths":99, "Physics":75, "Chemistry": 85},

{"Roll":12,"Maths":89, "Physics":85, "Chemistry": 88}

]

Create a new DataFrame from the list of new rows

Row_df = pd.DataFrame(newRows)

Split the original DataFrame into upper and lower parts

Df_upper = df.iloc[:2]

Df_lower = df.iloc[2:]

Concatenate the upper part, new rows, and lower part

Output_df = pd.concat([df_upper, row_df, df_lower], ignore_index=True)

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Appending a dictionary

To append a dictionary as a new row, you first need to create a Pandas dataframe and a dictionary with the new row values. Then, you can use the from_dict() function to build a dataframe with the dictionary row values. Finally, you can append the new dataframe to the original dataframe using the concat() function.

Here's an example code snippet:

Python

Import pandas as pd

Create a test dataframe

Df = pd.DataFrame({'student': ['Alex', 'Sam', 'Mary'], 'grade': ['A', 'B', 'C'], 'score': [45, 39, 35]})

Create a dictionary of new row values

New_student = {'student': 'Paige', 'grade': 'A', 'score': 49}

Build a dataframe with the dictionary row values

New_df = pd.DataFrame([new_student])

Append the new dataframe to the original dataframe

Df = pd.concat([df, new_df], ignore_index=True)

To append a dictionary as a new column, you first need to create a dictionary with the new column values. Then, you can use the from_dict() function to build a dataframe with the dictionary column values, specifying the orient parameter as 'index'. Finally, you can append the new dataframe as a new column to the original dataframe using the concat() function.

Here's an example code snippet:

Python

Import pandas as pd

Create a test dataframe

Df = pd.DataFrame({'student': ['Alex', 'Sam', 'Mary'], 'grade': ['A', 'B', 'C'], 'score': [45, 39, 35]})

Create a dictionary with the new column values

New_column = {'ethnicity': ['Asian', 'American', 'Hispanic']}

Build a dataframe with the dictionary column values

New_df = pd.DataFrame.from_dict(new_column, orient='index', columns=['ethnicity'])

Append the new dataframe as a new column to the original dataframe

Df = pd.concat([df, new_df], axis=1)

Handling missing keys and columns

When appending a dictionary to a dataframe, it's important to note that if the dictionary has fewer keys than the columns in the dataframe, the remaining columns will be assigned the value NaN in the rows where the dictionary is appended. Similarly, if the dictionary contains keys that are not present as column names in the dataframe, new columns will be added to the dataframe for each missing key.

Frequently asked questions