Sometimes, we have to create a pandas dataframe using a numpy array. In this tutorial, we will introduce you how to do.
How to create a pandas dataframe using numpy array?
A pandas dataframe may looks like:
To create a dataframe using numpy array, we should notice:
- we need a column name for each column
- each column saves a column of numpy array
We will use an example to show you how to do.
For example:
import pandas as pd import numpy as np data = np.random.random((5,4)) df = pd.DataFrame(data, columns = ["head1", "head2", "head3", "head4"]) print(df)
Here the shape of data is [5, 4], which means 5 rows and 4 columns.
It determines the column number of df is 4.
Meanwhile, we also can append a column for df as follows:
dx = np.array(["test1","test2","test3","test4","test5",]) df["head5"] = dx print(df)
Run this code, we will see:
head1 head2 head3 head4 head5 0 0.555170 0.139445 0.201438 0.922460 test1 1 0.525524 0.806295 0.264646 0.476339 test2 2 0.664801 0.844180 0.201969 0.399945 test3 3 0.764095 0.492824 0.916792 0.740180 test4 4 0.767567 0.222740 0.364453 0.062312 test5