Understanding data structures in Python is crucial for data analysis. Two popular libraries, NumPy and Pandas, offer powerful tools for this purpose. Sometimes, you may find the need to convert between their primary data structures: the NumPy array and the Pandas DataFrame. In this article, we’ll dive deep into how you can easily convert a NumPy array into a DataFrame with detailed examples.

**Creating a NumPy Array**

Let’s first create a simple NumPy array using the `numpy`

library. For our example, we’ll use data related to five individuals: Sachin, Ram, Abhilash, Mike, and Elaine.

```
import numpy as np
# Sample data: Age and Score for Sachin, Ram, Abhilash, Mike, and Elaine
data_array = np.array([
[25, 85],
[30, 88],
[29, 76],
[24, 90],
[27, 82]
])
print(data_array)
```

**Converting NumPy Array to DataFrame**

`pandas`

, converting a NumPy array to a DataFrame is straightforward. Here’s how you can do it:```
import pandas as pd
# Convert NumPy array to DataFrame
df = pd.DataFrame(data_array, columns=['Age', 'Score'], index=['Sachin', 'Ram', 'Abhilash', 'Mike', 'Elaine'])
print(df)
```

The columns argument helps us set the column names, while the index argument sets row labels for our data.

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