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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