Hello, readers! In this article, we will be focusing on **Python Pandas math functions for data analysis**, in detail. So, let us get started!

## Role of Pandas math functions in Data Analysis

In the domain of statistics and data analysis, the basic task is to analyze the data and draw observations out of them to have a better model built on it. For the same, it is necessary for us to explore functions that would help in the process of analyzing the data to draw meaning information out of it.

Python programming offers us with Pandas Module that contains various functions to enable us to analyze the data values.

Analysis of data simply means drawing out meaning information from the raw data source. This information enables us have an intimation about the distribution and structure of the data.

In the course of this article, we will be having a look at the below functions:

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

Let us have at each of them in the upcoming section!

In this article, we have made use of Bike Rental Prediction dataset. You can find the dataset here!

## 1. Pandas mean() function

Mean, as a statistical value, represents the entire distribution of data through a single value. Using **dataframe.mean()** function, we can get the value of mean for a single column or multiple columns i.e. entire dataset.

**Example:**

In this example, we have applied the mean() function on the entire dataset.

```
BIKE.mean()
```

**Output:**

As a result, the mean values for all the columns of the dataset is represented as shown below–

```
instant 366.000000
season 2.496580
yr 0.500684
mnth 6.519836
holiday 0.028728
weekday 2.997264
workingday 0.683995
weathersit 1.395349
temp 0.495385
atemp 0.474354
hum 0.627894
windspeed 0.190486
casual 848.176471
registered 3656.172367
cnt 4504.348837
dtype: float64
```

## 2. Pandas sum() function

Apart from mean() function, we can make use of **Pandas sum() function** to get the summation of the values of the columns at a larger scale. This enables us to have a broader perspective of the data in quantitative terms.

**Example:**

Here, we have calculated the summation of every column of the dataset by applying sum() function on the entire dataset.

```
BIKE.sum()
```

**Output:**

```
instant 267546
dteday 2011-01-012011-01-022011-01-032011-01-042011-0...
season 1825
yr 366
mnth 4766
holiday 21
weekday 2191
workingday 500
weathersit 1020
temp 362.126
atemp 346.753
hum 458.991
windspeed 139.245
casual 620017
registered 2672662
cnt 3292679
dtype: object
```

## 3. Pandas median() function

With median() function, we get the 50 percentile value or the central value of the set of data.

**Example:**

Here, we have applied median() function on every column of the dataset.

```
BIKE.median()
```

**Output:**

Here, we see the median values for every column of the dataset.

```
instant 366.000000
season 3.000000
yr 1.000000
mnth 7.000000
holiday 0.000000
weekday 3.000000
workingday 1.000000
weathersit 1.000000
temp 0.498333
atemp 0.486733
hum 0.626667
windspeed 0.180975
casual 713.000000
registered 3662.000000
cnt 4548.000000
```

## 4. Pandas min() and max() functions

With min() and max() functions, we can obtain the minimum and maximum values of every column of the dataset as well as the a single column of the dataframe.

**Example:**

Here, we have applied the max() function to obtain the maximum limit of every column of the dataset.

```
BIKE.max()
```

**Output:**

```
instant 731
dteday 2012-12-31
season 4
yr 1
mnth 12
holiday 1
weekday 6
workingday 1
weathersit 3
temp 0.861667
atemp 0.840896
hum 0.9725
windspeed 0.507463
casual 3410
registered 6946
cnt 8714
dtype: object
```

## 5. Pandas value_counts() function

With **value_counts() function**, we can fetch the count of every category or group present in a variable. It is beneficial with categorical variables.

**Example:**

```
BIKE.season.value_counts()
```

Here, we have applied value_counts() function on the season variable. As seen below, we get the count of every group present in the variable as a separate category.

**Output:**

```
3 188
2 184
1 181
4 178
```

## 6. Pandas describe() function

With describe() function, we get the below statistical information at once:

**count of the data values of every column****mean****standard deviation****minimum value****maximum value****25% value [1st quartile]****50% i.e. median****75% value [3rd quartile]**

**Example:**

```
BIKE.describe()
```

**Output:**

## Conclusion

By 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!! 🙂