I have a data set that i have brought over to Python,if someone could help me with what these codes may look like and i will try them with my own data.For each numeric column in your dataset, calculate the following:MeanMedianModeStandard deviationRangeInterquartile Range (IQR). Hint: use numpy.percentile() function to calculate the IQR using the following syntax for a column called age:q3, q1 = np.percentile(df['age'], [75 ,25])iqr = q3 - q1For each non-numeric column in your dataset, write the code to perform the following:The most frequent value for a columnThe unique values for a columnYou should also consider grouping your data based on some fields and perform some meaningful statistical analysis on the different groupsUsing Matplotlib and Seaborn libraries, visualize your dataset using meaningful graphs including, but not limited to, bar charts, histograms, line charts, scatter plots, pie charts.Mention some insights you have drawn from the visual graphs in part 1 Note 1: your visualization should include graphs for numeric features as well as non-numeric featuresNote 2: include your insights in markdown cells in the same Jupyter notebook

Load Necessary Libraries:Import the required libraries including pandas, numpy, matplotlib, seaborn,…

Answered: I have a data set that i have brought…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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I have a data set that i have brought over to Python,

if someone could help me with what these codes may look like and i will try them with my own data.

For each numeric column in your dataset, calculate the following:
1. Mean
2. Median
3. Mode
4. Standard deviation
5. Range
6. Interquartile Range (IQR). Hint: use numpy.percentile() function to calculate the IQR using the following syntax for a column called age:

q3, q1 = np.percentile(df['age'], [75 ,25])

iqr = q3 - q1

For each non-numeric column in your dataset, write the code to perform the following:
1. The most frequent value for a column
2. The unique values for a column
You should also consider grouping your data based on some fields and perform some meaningful statistical analysis on the different groups
Using Matplotlib and Seaborn libraries, visualize your dataset using meaningful graphs including, but not limited to, bar charts, histograms, line charts, scatter plots, pie charts.
Mention some insights you have drawn from the visual graphs in part 1

Note 1: your visualization should include graphs for numeric features as well as non-numeric features

Note 2: include your insights in markdown cells in the same Jupyter notebook

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