# xtreme

## Analyzing Data with Python: Counting how many times each value in a CSV occurs

Data analysis is an essential aspect of many fields, from business and research to sports and education. Python, with its versatile libraries, is a popular choice for data analysis tasks. In this blog post, we’ll explore a Python script that reads data from a CSV file and counts the occurrences of each value in 3 columns. The script can be a valuable tool for gaining insights into educational information datasets.

```#!/bin/python

import csv

# Create empty dictionaries
d_university = dict()
d_country = dict()
d_region = dict()

with open('XtremeScores.csv') as f:
# Loop through each line of the file
word = line[6]
# Check if the word is already in dictionary
if word in d_region:
# Increment count of word by 1
d_region[word] = d_region[word] + 1
else:
# Add the word to dictionary with count 1
d_region[word] = 1

word = line[5]
# Check if the word is already in dictionary
if word in d_country:
# Increment count of word by 1
d_country[word] = d_country[word] + 1
else:
# Add the word to dictionary with count 1
d_country[word] = 1

word = line[4]
# Check if the word is already in dictionary
if word in d_university:
# Increment count of word by 1
d_university[word] = d_university[word] + 1
else:
# Add the word to dictionary with count 1
d_university[word] = 1

sorted_university = sorted(d_university.items(), key=lambda x:x[1], reverse=True)
print(sorted_university[:10])
sorted_country = sorted(d_country.items(), key=lambda x:x[1], reverse=True)
print(sorted_country[:10])
sorted_region = sorted(d_region.items(), key=lambda x:x[1], reverse=True)
print(sorted_region[:10])

```

Let’s break down the code step by step:

```#!/bin/python

import csv
```

The script starts by importing the `csv` module, which is essential for handling comma-separated value (CSV) files.

```# Create empty dictionaries
d_university = dict()
d_country = dict()
d_region = dict()
```

Three empty dictionaries, `d_university`, `d_country`, and `d_region`, are created. These dictionaries will be used to store the counts of universities, countries, and regions, respectively.

```with open('XtremeScores.csv') as f:
```

The script opens a CSV file named ‘XtremeScores.csv’ using a `with` statement. The `csv.reader` object is used to read the contents of the file. We specify that the delimiter is a comma (`,`), and we don’t want to perform any special quoting.

```    # Loop through each line of the file
```

A `for` loop iterates through each line in the CSV file. The variable `line` contains the data for each row in the file.

```        word = line[6]
```

The code extracts the word at index 6 (zero-based index) from the current line. In this context, it typically represents a region.

```        # Check if the word is already in the dictionary
if word in d_region:
# Increment count of word by 1
d_region[word] = d_region[word] + 1
else:
# Add the word to the dictionary with count 1
d_region[word] = 1
```

The code checks if the extracted word (representing a region) is already present in the `d_region` dictionary. If it is, it increments the count by 1. If not, it adds the word to the dictionary with a count of 1. This process counts the occurrences of each region in the dataset.

The code repeats the same process for words representing countries (at index 5) and universities (at index 4), using the `d_country` and `d_university` dictionaries, respectively.

```sorted_university = sorted(d_university.items(), key=lambda x:x[1], reverse=True)
print(sorted_university[:10])
```

After counting the universities, the code sorts them in descending order of frequency and prints the top 10 universities based on their occurrence.

```sorted_country = sorted(d_country.items(), key=lambda x:x[1], reverse=True)
print(sorted_country[:10])
```

Similarly, it does the same for countries and prints the top 10 countries.

```sorted_region = sorted(d_region.items(), key=lambda x:x[1], reverse=True)
print(sorted_region[:10])
```

Finally, it sorts and prints the top 10 regions based on their occurrence.

In summary, this Python script provides a simple but effective way to analyze data in a CSV file, specifically counting universities, countries, and regions. It leverages dictionaries to maintain counts and uses the `csv` module for reading data from the file. This can be a useful starting point for more advanced data analysis tasks and visualization in Python.

## IEEEXtreme Uncut: Behind the Scenes. [Live-Stream]

The IEEEXtreme executive committee would like to invite you all to our upcoming live stream at IEEE.tv

During this event the 2020 IEEEXtreme Executive Committee will give student members an interactive “sneak peek” into the history, preparation, and fun the volunteer leadership is planning and executing for our flagship contest for student members across the globe.
During the 24 hour contest the committee feeds up the challenges, this live streamed event is your chance to challenge them with questions about this year’s contest on October 24.

Registration Page: https://engage.ieee.org/September-Student-Live-Sign-Up.html

Date and Time: 17 September, 1:00 pm EDT

## IEEEXtreme 11.0 Programming Competition

IEEEXtreme is a global challenge in which teams of IEEE Student members – advised and proctored by an IEEE member, and often supported by an IEEE Student Branch – compete in a 24-hour time span against each other to solve a set of programming problems.

IEEEXtreme 11.0 will take place on
14 October 2017 00:00:00 UTC.

Teams can be composed of up to three collegiate students who are current IEEE student members.

### Prizes:

1. Fame: Unlimited bragging rights and an item for your resume.
2. Fortune: The Grand Prize is a trip to the IEEE conference of your choice, anywhere in the world.

## IEEExtreme

IEEEXtreme is a global challenge in which teams of IEEE student members – supported by an IEEE Student Branch, advised and proctored by an IEEE Member – compete in a 24-hour time span against each other to solve a set of programming problems.