In this post, you will get a code sample for **creating **a **Pandas Dataframe **using a **Numpy array** with **Python **programming.

### Step 1: Load the Python Packages

import numpy as np import pandas as pd

### Step 2: Create a Numpy array

arr = np.array([[4, 7], [15,18], [18,21], [13,19], [10,15], [7,12], [4,6], [5,9], [8,10], [9,14], [13,15], [11,12], [12,17]])

This is how the array would look like:

array([[ 4, 7], [15, 18], [18, 21], [13, 19], [10, 15], [ 7, 12], [ 4, 6], [ 5, 9], [ 8, 10], [ 9, 14], [13, 15], [11, 12], [12, 17]])

### Step 3: Create a Transpose of Numpy Array

arr_tp = arr.transpose()

This is how the transpose would look like:

array([[ 4, 15, 18, 13, 10, 7, 4, 5, 8, 9, 13, 11, 12], [ 7, 18, 21, 19, 15, 12, 6, 9, 10, 14, 15, 12, 17]])

### Step 4: Create a Pandas Dataframe

df = pd.DataFrame({'col1': arr_tp[0], 'col2': arr_tp[1]})

Print the data using head command such as **df.head()**. This is how the data frame would look like:

col1 col2 0 4 7 1 15 18 2 18 21 3 13 19 4 10 15

In case, you would like to quickly plot the data and look for relationship, here are the command using **seaborn **package:

import seaborn as sns sns.scatterplot(x=df['col1'], y=df['col2'])

The above would print the following plot:

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