Hello, readers! In this article, we will be focusing on **Python Pandas math functions**, in detail.

So, let us begin!! 🙂

Table of Contents

## Python Pandas module – Quick overview

Python offers us with various modules to deal and manipulate the data according to the requirements.

One such module is python pandas module.

Pandas module is one of the most efficient library for data analysis and modelling. It offers us with DataFrame and Series data structure to store and manipulate the data in the form of rows and columns. Further, it consists of various functions to clean and process the data for modeling.

On the similar lines, when it comes to data analysis and modelling, we come across the need to have mathematical functions to moderate the data.

Let us have a look at a list of some cool and easy math functions offered by Python Pandas module.

## Pandas math functions

In the context of this topic, we will be focusing on the below mathematical functions offered by Pandas module–

**describe() function****value_counts() function****mean() and median() functions****sum() function****min() and max() functions**

We will be making use of the below dataset in the upcoming examples.

**Importing the above dataset into the Python environment::**

```
import pandas as pd
data = pd.read_csv("C:\\Users\\Downloads\\datasets_180_408_data.csv") # dataset
```

### 1. Pandas describe() function

With Python pandas describe() function, we can easily fetch the statistical information about the dataset with respect to the following parameters–

**value count****mean****median****minimum limit of data****maximum limit of data****inter-quartile range****standard deviation**, etc

Thus, if we wish to have a statistical analysis of data handy, describe() function is the best choice for us.

**Example–**

```
print(data.describe())
```

**Output–**

```
id radius_mean texture_mean perimeter_mean area_mean \
count 1.900000e+01 19.000000 19.000000 19.000000 19.000000
mean 4.049257e+07 16.081053 20.498947 106.725789 829.931579
std 4.293723e+07 2.942387 3.997922 19.297775 305.009648
min 8.423020e+05 11.420000 10.380000 77.580000 386.100000
25% 8.453085e+05 13.720000 18.935000 91.900000 578.100000
50% 8.490140e+05 15.850000 20.830000 103.600000 782.700000
75% 8.447960e+07 18.710000 22.925000 126.400000 1081.500000
max 8.486200e+07 20.570000 27.540000 135.100000 1326.000000
smoothness_mean compactness_mean concavity_mean
count 19.000000 19.000000 19.000000
mean 0.107596 0.164038 0.158438
std 0.016457 0.068554 0.067645
min 0.082060 0.066690 0.032990
25% 0.097250 0.105850 0.099460
50% 0.109600 0.159900 0.163900
75% 0.118500 0.215750 0.202250
max 0.142500 0.283900 0.300100
```

### 2. The sum() function

As we all know, the Pandas module deals with data in the form of rows and columns, thus in order to get the total value of every column, sum() function can be used.

With sum() function, we can get the numerical summation of every data column present in the dataset.

**Example–**

```
print(data.sum())
```

**Output–**

```
id 769358823
diagnosis MMMMMBMMBMBBMBMMBBB
radius_mean 305.54
texture_mean 389.48
perimeter_mean 2027.79
area_mean 15768.7
smoothness_mean 2.04432
compactness_mean 3.11673
concavity_mean 3.01032
dtype: object
```

### 3. Pandas mean() and median() function

One of the important statistical terms for analysis is mean and median.

So, with the Pandas module, we can make use of mean() and median() functions to get the mean as well as the median value of every individual data column easily.

**Example–**

```
print(data.mean())
print(data.median())
```

**Output–**

```
id 4.049257e+07
radius_mean 1.608105e+01
texture_mean 2.049895e+01
perimeter_mean 1.067258e+02
area_mean 8.299316e+02
smoothness_mean 1.075958e-01
compactness_mean 1.640384e-01
concavity_mean 1.584379e-01
dtype: float64
id 849014.0000
radius_mean 15.8500
texture_mean 20.8300
perimeter_mean 103.6000
area_mean 782.7000
smoothness_mean 0.1096
compactness_mean 0.1599
concavity_mean 0.1639
dtype: float64
```

### 4. The min() and max() function

- Pandas min() function enables us to have the minimum value limit for every column of the dataset handy.
- With max() function, all the maximum value for every individual column is displayed.

**Example–**

```
print(data.min())
print(data.max())
```

**Output–**

```
id 842302
diagnosis B
radius_mean 11.42
texture_mean 10.38
perimeter_mean 77.58
area_mean 386.1
smoothness_mean 0.08206
compactness_mean 0.06669
concavity_mean 0.03299
dtype: object
id 84862001
diagnosis M
radius_mean 20.57
texture_mean 27.54
perimeter_mean 135.1
area_mean 1326
smoothness_mean 0.1425
compactness_mean 0.2839
concavity_mean 0.3001
dtype: object
```

### 5. Pandas value_counts() function

In the domain of data science and analysis, when we deal with data variables, especially categorical type of variables, it is very necessary to understand the different trend in that type of variable.

That is, within every categorical variable, it is essential to identify the type of category/group within it.

With the value_counts() function, we can easily count the type of category or the frequency of the values within every variable/data column that we pass to the function.

**Example–**

In the below example, we have calculated the frequency of every data item of the column **radius_mean**, as shown below–

```
print(data.radius_mean.value_counts())
```

**Output–**

```
19.81 1
16.02 1
18.25 1
13.00 1
12.46 1
17.99 1
20.57 1
14.68 1
14.54 1
16.13 1
15.78 1
13.71 1
15.85 1
11.42 1
20.29 1
12.45 1
13.73 1
19.17 1
19.69 1
```

## Conclusion

With this, we have come to the end of this topic. Feel free to comment below in case you come across any question.

For more such posts related to Python programming, Stay tuned with us!

Till then, Happy Learning!! 🙂