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How to Create a DataFrame from Scratch in Python

Ze Mama
By Ze Mama
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How to Create a DataFrame from Scratch in Python

A DataFrame is one of the most versatile and widely used data structures provided by the pandas library in Python. It is designed for efficient handling, manipulation, and analysis of tabular data. In this article, we’ll explore different ways to create a DataFrame from scratch, depending on your data’s format and requirements.

What is a DataFrame?

A DataFrame is a two-dimensional, size-mutable, and heterogeneous data structure with labeled axes (rows and columns). Think of it as an in-memory spreadsheet or SQL table.

Importing pandas

To work with DataFrames, you first need to import the pandas library:

python
import pandas as pd

1. Creating a DataFrame from a Dictionary

One of the simplest and most common ways to create a DataFrame is by using a dictionary. Each key represents a column name, and the values are lists of data for that column.

Example:

python
import pandas as pd

data = {
'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35],
'City': ['New York', 'Los Angeles', 'Chicago']
}

df = pd.DataFrame(data)
print(df)

Output:

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

2. Creating a DataFrame from a List of Dictionaries

If you have data as a list of dictionaries, you can directly convert it into a DataFrame.

Example:

python
data = [
{'Name': 'Alice', 'Age': 25, 'City': 'New York'},
{'Name': 'Bob', 'Age': 30, 'City': 'Los Angeles'},
{'Name': 'Charlie', 'Age': 35, 'City': 'Chicago'}
]

df = pd.DataFrame(data)
print(df)

Output:

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

3. Creating a DataFrame from a List of Lists

You can create a DataFrame from a list of lists, specifying the column names.

Example:

python
data = [
['Alice', 25, 'New York'],
['Bob', 30, 'Los Angeles'],
['Charlie', 35, 'Chicago']
]

df = pd.DataFrame(data, columns=['Name', 'Age', 'City'])
print(df)

Output:

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

4. Creating a DataFrame from a NumPy Array

If you have data as a NumPy array, you can convert it into a DataFrame by specifying the column names.

Example:

python
import numpy as np

data = np.array([
['Alice', 25, 'New York'],
['Bob', 30, 'Los Angeles'],
['Charlie', 35, 'Chicago']
])

df = pd.DataFrame(data, columns=['Name', 'Age', 'City'])
print(df)

Output:

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

5. Creating a DataFrame from a Dictionary of Series

You can also create a DataFrame using a dictionary of pandas Series. This method allows for more granular control over indices.

Example:

python
data = {
'Name': pd.Series(['Alice', 'Bob', 'Charlie']),
'Age': pd.Series([25, 30, 35]),
'City': pd.Series(['New York', 'Los Angeles', 'Chicago'])
}

df = pd.DataFrame(data)
print(df)

Output:

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

6. Creating an Empty DataFrame

To create a DataFrame without any data, you can use the following approach:

Example:

python
df = pd.DataFrame(columns=['Name', 'Age', 'City'])
print(df)

Output:

less
Empty DataFrame
Columns: [Name, Age, City]
Index: []

You can later populate this DataFrame using methods like df.loc[] or df.append().

7. Creating a DataFrame from Custom Index

You can specify a custom index while creating a DataFrame.

Example:

python
data = {
'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35],
'City': ['New York', 'Los Angeles', 'Chicago']
}

df = pd.DataFrame(data, index=['a', 'b', 'c'])
print(df)

Output:

css
Name Age City
a Alice 25 New York
b Bob 30 Los Angeles
c Charlie 35 Chicago

8. Creating a DataFrame from a CSV/Excel Template

While not strictly creating a DataFrame from scratch, you can use templates with predefined column names and fill them later.

Example: CSV Template

python
df = pd.read_csv('template.csv')
print(df)

Conclusion

Creating a DataFrame from scratch in pandas is straightforward, with multiple methods available based on your data’s structure:

  • Use dictionaries for labeled data.
  • Use lists or arrays for simpler, indexed data.
  • For dynamic or complex data, explore dictionaries of Series or NumPy arrays.

The flexibility of pandas makes it easy to build, manipulate, and analyze DataFrames efficiently.

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