Gain insights into performing predictive data analysis using Python. Learn about tools like NumPy, Pandas, Matplotlib, and Seaborn, and applying techniques to real-world financial and advertisement projects.

seaborn.tar.gz

seaborn

In this course, you will learn how to perform predictive data analysis using Python. The ideal audience is those who want to start their careers as data analysts. The main goal of this course is to show you how to use statistics to draw useful insights from data which can help in predicting future behavior or patterns.

Beyond that, you’ll learn all the tools of the trade that data scientists use everyday including: NumPy, Pandas, Matplotlib, and Seaborn. You’ll learn not only how to extract meaningful insights from data, but you’ll also learn how to create stunning visualizations that you can use for reports.

Various datasets of real-world scenarios are used in each lesson to get you accustomed to handling any type of data. At the end of the course, you will work on two real-world projects that demonstrate how data analysis techniques are being used in the financial and advertisement sector to generate revenue.

Predictive Data Analysis with Python

# Problem statement

We have to implement the function `Sum_Swap(df)`. The `df` is the `DataFrame` on which operations will be performed. The task is to create a new column and a new row and then swap their values. The following steps are performed to calculate the values for the new row and column before swapping:

1. The sum of the minimum and maximum values of each row are calculated.

2. These values are assigned to the new column `row_sum`.

3. The sum of the minimum and maximum value of each column are calculated.

4. These values are assigned to the new row `col_sum`.

5. Finally, the new row and column values are swapped.

## Input

The input to the function is a `DataFrame` with random data. The following is just an example of what the input `DataFrame` would look like.

```
  0  1  2  3 
0 12 2  3  44
1 40 1  34 9
2 6  99 56 69
3 2  24 4  71

```

## Output

The output is a `DataFrame` with a new row and column. 
The following is an example of what the output of the above input `DataFrame` would look like after summing and swapping the row and column.

```
        0  1  2   3   row_sum

0       12 2  3   44  42
1       40 1  34  9   100
2       6  99 56  69  59
3       2  24 4   71  80
col_sum 46 41 105 73  NaN

```

# Coding exercise
Write your code below. It is recommended​ that you try solving the exercise yourself before viewing the solution.

In this challenge, pandas is used to sum the minimum and maximum values of the rows and columns of a DataFrame, which are then swapped.

Getting Started

Numpy for Python

Pandas for Python

Statistics for Data Analysis

Data Wrangling

Visualizing the Data

Data Scraping

Project #1

Project #2

Conclusion

Predictive Data Analysis Exam

Challenge: Summing and Swapping

Problem statement