Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about **How can I get descriptive statistics of a NumPy array** **in Python**. So Here I am Explain to you all the possible Methods here.

Without wasting your time, Let’s start This Article.

Table of Contents

## How can I get descriptive statistics of a NumPy array?

**How can I get descriptive statistics of a NumPy array?**This is not a pretty solution, but it gets the job done. The problem is that by specifying multiple dtypes, you are essentially making a 1D-array of tuples (actually

`np.void`

), which cannot be described by stats as it includes multiple different types, incl. strings.**get descriptive statistics of a NumPy array**This is not a pretty solution, but it gets the job done. The problem is that by specifying multiple dtypes, you are essentially making a 1D-array of tuples (actually

`np.void`

), which cannot be described by stats as it includes multiple different types, incl. strings.

## Method 1

import pandas as pd import numpy as np df_describe = pd.DataFrame(dataset) df_describe.describe()

please note that dataset is your np.array to describe.

import pandas as pd import numpy as np df_describe = pd.DataFrame('your np.array') df_describe.describe()

## Method 2

This is not a pretty solution, but it gets the job done. The problem is that by specifying multiple dtypes, you are essentially making a 1D-array of tuples (actually `np.void`

), which cannot be described by stats as it includes multiple different types, incl. strings.

This could be resolved by either reading it in two rounds, or using pandas with `read_csv`

.

If you decide to stick to `numpy`

:

import numpy as np a = np.genfromtxt('sample.txt', delimiter=",",unpack=True,usecols=range(1,9)) s = np.genfromtxt('sample.txt', delimiter=",",unpack=True,usecols=0,dtype='|S1') from scipy import stats for arr in a: #do not need the loop at this point, but looks prettier print(stats.describe(arr)) #Output per print: DescribeResult(nobs=6, minmax=(0.34999999999999998, 0.70999999999999996), mean=0.54500000000000004, variance=0.016599999999999997, skewness=-0.3049304880932534, kurtosis=-0.9943046886340534)

Note that in this example the final array has `dtype`

as `float`

, not `int`

, but can easily (if necessary) be converted to int using `arr.astype(int)`

**Summery**

It’s all About this issue. Hope all Methods helped you a lot. Comment below Your thoughts and your queries. Also, Comment below which Method worked for you? Thank You.

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