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How to Create a Data Frame in Python using Pandas

Published in Pandas DataFrame Creation 4 mins read

Creating a data frame is a fundamental step in data analysis, particularly when working with tabular data in Python. The most common and powerful library for this task is Pandas, which provides highly optimized data structures like DataFrame objects.

To create a data frame, you typically define your data and then use the pd.DataFrame() constructor. This process is straightforward and involves a few key steps to prepare and structure your information into a robust, tabular format.

Step-by-Step Guide to Data Frame Creation

Follow these steps to construct a data frame with 3 columns and 5 rows using the Pandas library:

1. Import the Pandas Library

The first step is to import the Pandas library, conventionally aliased as pd. This makes it easier to reference Pandas functions throughout your code.

import pandas as pd

2. Define Your Data

Next, you need to define the data that will populate your data frame. This data can be in various formats, such as a dictionary, a list of lists, or a NumPy array. For structured data like a data frame, a dictionary where keys represent column names and values are lists of data (each list representing a column) is often intuitive.

According to the requirements, your data frame needs to have 3 columns and 5 rows. Let's define our data in a variable named d as specified.

d = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
    'Age': [25, 30, 35, 40, 28],
    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Miami']
}

In this example:

  • 'Name', 'Age', and 'City' are the 3 column names.
  • Each list contains 5 elements, ensuring 5 rows of data for each column.

3. Create the Data Frame using pd.DataFrame()

With your data defined, you can now create the data frame using the pd.DataFrame() function. You pass your data variable (d in this case) as an argument to this function.

df = pd.DataFrame(d)

This line of code converts your dictionary d into a Pandas DataFrame object, df.

4. Verify Data Frame Dimensions

After creation, the data frame df will contain 3 columns (Name, Age, City) and 5 rows, perfectly aligning with the specified requirements. Each row represents a record, and each column represents a specific attribute.

5. Print the Data Frame Output

Finally, to view the contents and structure of your newly created data frame, use the print() function. This will display the data frame in a readable, tabular format in your console or environment.

print(df)

Complete Code Example

Here is the complete Python code demonstrating how to create the data frame:

# 1. Import the Pandas library as pd
import pandas as pd

# 2. Define data with column and rows in a variable named d
# The data frame will contain 3 columns and 5 rows
d = {
    'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Eve'],
    'Age': [25, 30, 35, 40, 28],
    'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Miami']
}

# 3. Create a data frame using the function pd.DataFrame()
df = pd.DataFrame(d)

# 4. Print the data frame output with the print() function
print("Your newly created DataFrame:")
print(df)

Output of the Data Frame

When you run the code, the output will clearly display the structured data:

Your newly created DataFrame:
      Name  Age         City
0    Alice   25     New York
1      Bob   30  Los Angeles
2  Charlie   35      Chicago
3    David   40      Houston
4      Eve   28        Miami

Key Considerations for Data Frame Creation

  • Column Names: When using a dictionary, the keys automatically become your column names, making it very intuitive.
  • Data Types: Pandas intelligently infers data types for each column (e.g., 'object' for strings, 'int64' for integers). You can explicitly set data types if needed during creation.
  • Indexing: By default, Pandas assigns a numerical index (0, 1, 2, ...) to the rows. You can specify a custom index if your data has a natural identifier for rows.
  • Flexibility: Data frames are highly flexible and can be modified, filtered, and analyzed using a vast array of Pandas functions, making them indispensable for data science tasks.

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