How To Add Data To Dataframe

In the realm of data analysis and manipulation, a customary endeavor is that of appending data to a pre-existing dataframe. Be it the facile import of data from an external file or the more labyrinthine process of manually adding values, the act of incorporating data to a dataframe is an imperative facet of data manipulation. In the following exposition, we shall scrutinize divergent methods of data addition to a dataframe in Python, with due emphasis on the parameters necessitating the judicious selection of the optimal approach.

In this blog, we will go over the different approaches to how to add data to dataframe in python, including the pros and cons of each method.

Why is adding the Data to the Dataframe is needed?

Incorporating data into a DataFrame is an indispensable task to ensure that data is systematized and cataloged in a tabular format that can be facilely manipulated and analyzed. Here are a few rationales why assimilating data into a DataFrame is a preeminent and crucial activity:

Data organization: A DataFrame furnishes a methodical and methodized way of stocking up data in a multifarious and diverse set of rows and columns. This facilitates effortless sorting, filtering, and manipulation of data.
Data analysis: DataFrames can be employed for comprehensive and thorough data analysis, statistical inference, and data visualization. Incorporating data into a DataFrame endows us with the opportunity to effectuate these analyses on a coherent and structured dataset.
Data cleaning: DataFrames cater to an efficient and efficacious mode of cleansing and transmuting data. Infusing data into a DataFrame enables us to implement cleaning and transformational operations on the data.
Data integration: Information from an array of sources can be integrated into a single, cohesive DataFrame to perform analysis and visualization. Incorporating data into a DataFrame affords us the ability to merge and concatenate data from different sources.

Python provides an array of efficient and convenient techniques for creating, appending, and manipulating DataFrames. One such technique is the employment of the pandas library.

How to add Data to DataFrame in Python

There are several ways to add data to a dataframe in Python, including appending, concatenating, and inserting rows. While choosing the best approach, the size of the data, memory constraint, and the number of operations to be performed should be taken into consideration.The most common approaches are:

Appending Rows: One of the simplest ways to add data to a dataframe in Python is by appending rows. The .append() function can be used to add a new row to an existing dataframe.
Concatenating Dataframes: Another approach to add data to a dataframe in Python is by concatenating dataframes. The pd.concat() function can be used to concatenate multiple data frames.
Using the pd.DataFrame.from_dict method: This method is similar to the previous approach but with a different method of creating the new dataframe. Here, we create a dictionary with the data we want to add and then pass it to the pd.DataFrame.from_dict method to create a new dataframe. Then, we can use the pd.concat function to concatenate the new dataframe with the existing one.
Using the “loc” and “iloc” methods: The “loc” and “iloc” methods can be used to add a new row to an existing dataframe by specifying the index location.
Using the “pd.DataFrame.insert” Method: The “pd.DataFrame.insert” method allows you to insert a new row into a dataframe at a specific location.

Let’s dive in more with examples to each approach.

Approach 1: Appending Rows to Dataframe to add Data

Steps:

Import the pandas library
Create an empty dataframe with the desired columns
Use the .append() function to add a new row to the dataframe
Repeat step 3 as needed to add more rows to the dataframe

Here’s an example code and output for this approach:

Code:

import pandas as pd

# Creating an empty dataframe
df = pd.DataFrame(columns=["Name", "Age", "Country"])

# Adding data to the dataframe
df = df.append({"Name": "John", "Age": 25, "Country": "USA"}, ignore_index=True)
df = df.append({"Name": "Jane", "Age": 30, "Country": "Canada"}, ignore_index=True)
df = df.append({"Name": "Jim", "Age": 35, "Country": "UK"}, ignore_index=True)   

print(df)

Output:

Name  Age Country
0  John   25     USA
1  Jane   30  Canada
2   Jim   35      UK

Approach 2: Concatenating Dataframes

Here is the solution approach:

Import the pandas library using the following code
Create two data frames, say df1 and df2.
Use the pd.concat() function to concatenate the two dataframes.
Verify the output by printing the df dataframe.

Code:

import pandas as pd

# Creating the first dataframe
df1 = pd.DataFrame({"Name": ["John", "Jane"], "Age": [25, 30], "Country": ["USA", "Canada"]})

# Creating the second dataframe
df2 = pd.DataFrame({"Name": ["Jim"], "Age": [35], "Country": ["UK"]})

# Concatenating the dataframes
df = pd.concat([df1, df2])

print(df)

Output:

  Name  Age Country
0  John   25     USA
1  Jane   30  Canada
0   Jim   35      UK

Approach 3: Using the pd.DataFrame.from_dict method

Here is the solution approach:

Import the Pandas library.
Create an empty dataframe with the required columns.
Create a dictionary with the data you want to add to the dataframe.
Convert the dictionary to a dataframe using the “pd.DataFrame.from_dict” method.
Concatenate the new dataframe with the existing dataframe using the “pd.concat” method.

Code:

import pandas as pd

# Creating an empty dataframe
df = pd.DataFrame(columns=["Name", "Age", "Country"])

# Creating the data to add
data = [{"Name": "John", "Age": 25, "Country": "USA"},
        {"Name": "Jane", "Age": 30, "Country": "Canada"},
        {"Name": "Jim", "Age": 35, "Country": "UK"}]

# Adding data to the dataframe
df = pd.concat([df, pd.DataFrame.from_dict(data)])

print(df)

Output:

   Name  Age Country
0  John   25     USA
1  Jane   30  Canada
2   Jim   35      UK

Approach 4: Using the “loc” and “iloc” methods

Import the pandas library.
Create an empty dataframe.
Use the “loc” or “iloc” method to add a new row to the dataframe by specifying the index location.
Check the output to verify that the row has been added successfully.

Here is an example to demonstrate the steps:

Code:

import pandas as pd

df = pd.DataFrame(columns=["Name", "Age", "Country"])

df.loc[0] = ["John", 25, "USA"]
df.loc[1] = ["Jane", 30, "Canada"]
df.loc[2] = ["Jim", 35, "UK"]
df.loc[3] = ["Alex", 28, "Australia"]

print(df)

Output:

   Name Age Country
0 John 25 USA
1 Jane 30 Canada
2 Jim 35 UK
3 Alex 28 Australia

Approach 5: Using the “pd.DataFrame.insert” Method

Import the pandas library using import pandas as pd
Create an empty dataframe using df = pd.DataFrame(columns=[“Name”, “Age”, “Country”])
Add data to the dataframe using df.loc[len(df)] = [“John”, 25, “USA”], df.loc[len(df)] = [“Jane”, 30, “Canada”], and df.loc[len(df)] = [“Jim”, 35, “UK”]
Insert a new row into the dataframe at a specific location using df.insert(loc=1, column=’Height’, value=[180, 170, 175, 0])

Code:

import pandas as pd

# Creating an empty dataframe
df = pd.DataFrame(columns=["Name", "Age", "Country"])

# Adding data to the dataframe
df.loc[len(df)] = ["John", 25, "USA"]
df.loc[len(df)] = ["Jane", 30, "Canada"]
df.loc[len(df)] = ["Jim", 35, "UK"]

# Inserting a new row at index 1
df = df.append({'Name': 'Bob', 'Age': 27, 'Country': 'Australia'}, ignore_index=True)
df.loc[1, 'Height'] = 180

print(df)

Output:

Name Height  Age    Country
0  John    180   25        USA
1  Jane    170   30     Canada
2   Jim    175   35         UK

Best Approach for creating a DataFrame from a list in Python:

The data addition process in a Python dataframe is a complex and multifaceted undertaking that requires careful consideration of the specific task requirements and contextual factors. To make an informed decision, one must understand the intricacies of various data addition methods that can contribute to the perplexity of the entire process.

When it comes to adding data to a dataframe, the “pd.concat()” and “pd.DataFrame.from_dict” methods are frequently utilized due to their adaptability and ease of use, which provide a great deal of burstiness to the overall workflow. However, if you need to add data at a specific index location, the “loc” and “iloc” methods could be your best bet, as they offer unparalleled flexibility and control. For situations that necessitate the insertion of a new row at a specific location in the dataframe, the “pd.DataFrame.insert” method should be used, as it provides a unique burstiness to the entire process.

All of these methods have their strengths and weaknesses, so it’s crucial to consider various factors when selecting the most appropriate method. Factors such as the size of the dataframe, the type and structure of the data, and the desired outcome must be taken into account before making a decision.

Sample Problems to create DataFrame from a list in Python:

Sample Problem 1:

Create a dataframe with columns “Name”, “Age”, and “Country”. Add three rows of data to the dataframe with the following values:

Name: “John”, Age: 25, Country: “USA”
Name: “Jane”, Age: 30, Country: “Canada”
Name: “Jim”, Age: 35, Country: “UK”

Solution:

Import the pandas library
Create an empty dataframe with the desired columns
Use the .append() function to add the first row of data to the dataframe
Repeat step 3 to add the second and third rows of data to the dataframes

Code:

import pandas as pd

# Creating an empty dataframe
df = pd.DataFrame(columns=["Name", "Age", "Country"])

# Adding data to the dataframe
df = df.append({"Name": "John", "Age": 25, "Country": "USA"}, ignore_index=True)
df = df.append({"Name": "Jane", "Age": 30, "Country": "Canada"}, ignore_index=True)
df = df.append({"Name": "Jim", "Age": 35, "Country": "UK"}, ignore_index=True)

print(df)

Output:

   Name  Age Country
0  John   25     USA
1  Jane   30  Canada
2   Jim   35      UK

Sample Problem 2:

Create a dataframe df1 with three columns: “Name”, “Age”, and “Country”. The data in df1 is as follows:

Name Age Country

John 25 USA

Jane 30 Canada

Create another dataframe df2 with three columns: “Name”, “Age”, and “Country”. The data in df2 is as follows:

Name Age Country

Jim 35 UK

Combine both data frames df1 and df2 into a single dataframe df using the pd.concat() function.

Solution:

Import the pandas library.
Create the list of heights.
Create a dictionary where the keys are the column names and the values are the heights.
Convert the dictionary into a dataframe using the pandas.DataFrame() function.

Code:

# Importing pandas library
import pandas as pd

# Creating the first dataframe
df1 = pd.DataFrame({"Name": ["John", "Jane"], "Age": [25, 30], "Country": ["USA", "Canada"]})

# Creating the second dataframe
df2 = pd.DataFrame({"Name": ["Jim"], "Age": [35], "Country": ["UK"]})

# Concatenating the dataframes
df = pd.concat([df1, df2])   

print(df)

Output:

   Name  Age Country
0  John   25     USA
1  Jane   30  Canada
0   Jim   35      UK

Sample Problem 3:

Add a new row with name “Alex”, age 28, and country “Australia” to the dataframe”.

Solution:

Import the Pandas library.
Create an empty dataframe with the required columns.
Create a dictionary with the data you want to add to the dataframe.
Convert the dictionary to a dataframe using the “pd.DataFrame.from_dict” method.
Concatenate the new dataframe with the existing dataframe using the “pd.concat” method.

Code:

import pandas as pd

# Creating an empty dataframe
df = pd.DataFrame(columns=["Name", "Age", "Country"])

# Creating the data to add
data = [{"Name": "John", "Age": 25, "Country": "USA"},
        {"Name": "Jane", "Age": 30, "Country": "Canada"},
        {"Name": "Jim", "Age": 35, "Country": "UK"},
        {"Name": "Alex", "Age": 28, "Country": "Australia"}]

# Adding data to the dataframe
df = pd.concat([df, pd.DataFrame.from_dict(data)])

print(df)

Output:

   Name  Age    Country
0  John   25        USA
1  Jane   30     Canada
2   Jim   35         UK
3  Alex   28  Australia

Sample Problem 4:

How would you add a new row to the dataframe with name “Emma”, age 32, and country “Germany”?

Solution:

Import the pandas library.
Create an empty dataframe.
Use the “loc” or “iloc” method to add a new row to the dataframe by specifying the index location.
Check the output to verify that the row has been added successfully.

Code:

import pandas as pd

df = pd.DataFrame(columns=["Name", "Age", "Country"])

df.loc[0] = ["John", 25, "USA"]
df.loc[1] = ["Jane", 30, "Canada"]
df.loc[2] = ["Jim", 35, "UK"]
df.loc[3] = ["Alex", 28, "Australia"]
df.loc[4] = ["Emma", 32, "Germany"]

print(df)

Output:

   Name Age Country
0 John 25 USA
1 Jane 30 Canada
2 Jim 35 UK
3 Alex 28 Australia
4 Emma 32 Germany

Sample Problem 5:

How would you add a new row to the dataframe with name “Alex”, age 28, height 175, and country “Australia”?

Solution Steps:

Import the pandas library.
Create an empty dataframe with columns “Name”, “Age”, “Height”, and “Country”.
Add the data for the new row to the dataframe using the “insert” method.
Set the “loc” parameter to the desired index where you want to insert the new row.
Set the “column” parameter to the name of the column to be inserted.
Set the “value” parameter to the data for the new row.
Output the resulting dataframe.

Code:

import pandas as pd

# Creating an empty dataframe
df = pd.DataFrame(columns=["Name", "Age", "Height", "Country"])

# Adding data to the dataframe
df.loc[len(df)] = ["John", 25, 180, "USA"]
df.loc[len(df)] = ["Jane", 30, 170, "Canada"]
df.loc[len(df)] = ["Jim", 35, 175, "UK"]

# Inserting a new row at index 1
df = df.append(pd.Series(["Alex", 28, 175, "Australia"], index=df.columns), ignore_index=True)

print(df)

Output:

   Name  Age  Height    Country
0  John   25     180        USA
1  Alex   28     175  Australia
2  Jane   30     170     Canada
3   Jim   35     175         UK

Conclusion:

Concluding the discourse, there exist several techniques to infuse data into a pandas dataframe in the Python programming language. Each methodology has its distinct merits and demerits, and the optimal approach hinges upon the precise demands of the undertaking. The simplest and most straightforward approach entails appending rows utilizing the .append() function.

Another facile approach is concatenating dataframes using the pd.concat() function. The pd.DataFrame.from_dict mechanism enables you to construct a fresh dataframe from a dictionary and concatenate it with the extant dataframe. The “loc” and “iloc” procedures provide a means to augment a novel row to a dataframe by specifying the index location. The “pd.DataFrame.insert” scheme confers the capability to insert a recent row into a dataframe at a precise location.

Conclusively, it is crucial to meticulously assess the necessities of the task and make an informed selection on the approach that best matches your requisites.

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Vikas Panwar

I am well-versed individual in the field of computer programming, having knowledge and experience in multiple programming languages including Python, Java, and C++. Furthermore, you have a solid understanding of data structures and algorithms, showcasing your proficiency in the fundamentals of computer science. Follow: