How To Convert To Dataframe In Python

Converting data into a suitable format is a vital step in any data analysis process, and DataFrames are one of the most important data structures in Python. A DataFrame is a two-dimensional data structure, like a table in a spreadsheet, consisting of rows and columns.

It has powerful functionalities for handling large datasets, such as filtering, sorting, and aggregating data efficiently. In this tutorial, we’ll show you how to convert different data types into a DataFrame using Python’s Pandas library.

Step 1: Install Pandas

Before we can start converting data into a DataFrame, we need to have the Pandas library installed in your Python environment. If you haven’t installed it yet, simply run the following command in your terminal or command prompt:

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pip install pandas

This will install the latest version of the Pandas library.

Once the installation is complete, you can now import Pandas in your Python script using the following line:

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import pandas as pd

We’ll use the abbreviation pd for convenience when referring to Pandas functions and methods throughout the tutorial.

Step 2: Create a DataFrame from a Dictionary

A common way to create a DataFrame is by converting a Python dictionary. The keys of the dictionary will become the columns of the DataFrame, while the values will be the corresponding data.

Here’s an example of how to create a DataFrame from a dictionary:

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data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "San Francisco", "Los Angeles"]
}
 
df = pd.DataFrame(data)
print(df)

The output of the code:

  Name  Age           City
0  Alice   25       New York
1    Bob   30  San Francisco
2  Charlie   35    Los Angeles

Step 3: Create a DataFrame from a List of Lists

Another option for creating a DataFrame is to provide a list of lists, with each inner list representing a row of data. We’ll also need to specify the column names by passing a list to the columns parameter.

Here’s an example:

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data = [
    ["Alice", 25, "New York"],
    ["Bob", 30, "San Francisco"],
    ["Charlie", 35, "Los Angeles"]
]
 
columns = ["Name", "Age", "City"]
 
df = pd.DataFrame(data, columns=columns)
print(df)

The output of the code:

      Name  Age           City
0    Alice   25       New York
1      Bob   30  San Francisco
2  Charlie   35    Los Angeles

Step 4: Create a DataFrame from a CSV File

Another common use case is reading data from a CSV (Comma-Separated Values) file. You can easily create a DataFrame from a CSV file using the read_csv function from Pandas.

Assuming we have a CSV file called “data.csv” with the following content:

Name,Age,City
Alice,25,New York
Bob,30,San Francisco
Charlie,35,Los Angeles

You can read the CSV file and create a DataFrame using the following code:

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filename = "data.csv"
df = pd.read_csv(filename)
print(df)

The output of the code:

      Name  Age           City
0    Alice   25       New York
1      Bob   30  San Francisco
2  Charlie   35    Los Angeles

Step 5: Create a DataFrame from an Excel File

Like CSV files, Excel files can also be read and converted into a DataFrame using the Pandas library. To read Excel files, you’ll first need to install the openpyxl package by running:

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pip install openpyxl

Assuming you have an Excel file called “data.xlsx” with the same content as the CSV file in the previous step, you can create a DataFrame by using the following code:

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filename = "data.xlsx"
df = pd.read_excel(filename, engine="openpyxl")
print(df)

The output of the code:

      Name  Age           City
0    Alice   25       New York
1      Bob   30  San Francisco
2  Charlie   35    Los Angeles

Full Code

Here’s the full code with all the examples from this tutorial:

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import pandas as pd
 
# Example 1: Create DataFrame from Dictionary
data = {
    "Name": ["Alice", "Bob", "Charlie"],
    "Age": [25, 30, 35],
    "City": ["New York", "San Francisco", "Los Angeles"]
}
df1 = pd.DataFrame(data)
print("DataFrame from Dictionary:")
print(df1)
print()
 
# Example 2: Create DataFrame from List of Lists
data = [
    ["Alice", 25, "New York"],
    ["Bob", 30, "San Francisco"],
    ["Charlie", 35, "Los Angeles"]
]
columns = ["Name", "Age", "City"]
df2 = pd.DataFrame(data, columns=columns)
print("DataFrame from List of Lists:")
print(df2)
print()
 
# Example 3: Create DataFrame from CSV File
filename = "data.csv"
df3 = pd.read_csv(filename)
print("DataFrame from CSV File:")
print(df3)
print()
 
# Example 4: Create DataFrame from Excel File
filename = "data.xlsx"
df4 = pd.read_excel(filename, engine="openpyxl")
print("DataFrame from Excel File:")
print(df4)
print()

Conclusion

In this tutorial, we’ve demonstrated how to convert various data types, such as dictionaries, lists of lists, CSV files, and Excel files, into DataFrames using Python’s Pandas library. With a strong understanding of these methods, you’ll be well-prepared to handle different data formats and carry out efficient data analysis in Python.