Python csv data analysis

Read in CSV Files for Data Analysis¶

Python is a great programming language for data analysis.

CSV files are comma-separated values to store data, similar to a table format.

The CSV format is a common import and export format for spreadsheets and databases. You’ll likely encounter it early on in the field of data science.

We’ll cover how to read in data from a CSV in which a CSV file is in your Python program’s directory/folder on your computer.

Import CSV module¶

Read in bike rides CSV¶

In my directory as this Python program, I created a CSV named bike_rides.csv .

open is a Python built-in function to open a file on your local computer with the argument being the file path. A default argument of open is mode=r to open the contents of a file with only permission to read the file, not write to it or perform a different operation. We’ll provide an alias as file so we can easily use that name in another operation.

From the csv module in Python, we’ll call the reader method and pass in file to create a csv reader object.

Once we have this csv reader object, we can iterate over the rows of our CSV file and print the output.

with open('bike_rides.csv') as file: csv_reader_object = csv.reader(file) for row in csv_reader_object: print("CSV row: ".format(row)) 

with open(‘bike_rides.csv’) as file: csv_reader_object = csv.reader(file) for row in csv_reader_object: print(«CSV row: <0>«.format(row))

CSV row: ['ride_number', 'date', 'miles_rode', 'ride_duration'] CSV row: ['1', '4-02-2018', '5.1', '29.4'] CSV row: ['2', '4-03-2018', '3.3', '20'] CSV row: ['3', '4-04-2018', '4.5', '21.8']

Each row in our CSV is a list of strings since there are single-quotes around each value.

Calculate Descriptive Statistics on Bike Rides¶

Commonly, when we read in data from a CSV, we may want to analyze the data or perform some analysis. We can perform these calculations as we loop over all data in our CSV.

Count the number of bike rides¶

Our csv_reader_object has a method called line_num that returns the number of lines in our CSV. If we perform this operation, we’re returned 4 .

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How to visualize CSV files using Python

The ability to plot CSV files using Python is important because it enables you to analyze, visualize, and process data efficiently and effectively. This, in turn, facilitates data-driven decision-making and better communication of results.

There are various benefits of using both CSV and Python for analysis:

1. Versatility: CSV files are a common format for storing and exchanging structured data. They are used across various industries and applications, making it important to be able to work with them in a popular programming language like Python.
2. Data analysis: Python offers powerful libraries for data analysis, such as Pandas, NumPy, and SciPy. By being able to read, process, and plot data from CSV files, you can leverage these libraries to gain insights, perform statistical analyses, and make data-driven decisions.
3. Data visualization: Visualization is crucial for understanding complex datasets and identifying patterns, trends, or anomalies. Python provides several libraries for creating plots and visualizations, like Matplotlib, Seaborn, and Plotly. By plotting data from CSV files, you can effectively communicate your findings and present your results in a more digestible way.
4. Automation: Python is an excellent choice for automating tasks, including those related to data processing and analysis. By being able to work with CSV files, you can develop scripts to automate repetitive tasks, such as importing data, preprocessing, and generating reports.
5. Compatibility: Python is compatible with many platforms and systems, making it an ideal choice for working with CSV files across different environments. Its extensive ecosystem of libraries and tools further enhances its suitability for a wide range of tasks.

We will explore the importance of working with CSV files in Python and discuss three approaches to visualize data using Pandas, Matplotlib, Seaborn, and Plotly. We will discover the benefits and versatility of these powerful libraries while acknowledging that our exploration only scratches the surface of their full capabilities for data visualization.

What is CSV?

A CSV (Comma-Separated Values) file format is a widely used plain-text file format that stores tabular data (numbers and text) in a simple structure. As the name suggests, the values in a CSV file are separated by commas, which act as delimiters. However, other delimiters such as tabs or spaces can also be used in some cases.

Each line in a CSV file typically represents a row of data, and each value separated by a comma corresponds to a cell in that row. The first row often serves as a header, describing the data in each column. CSV files can be opened and edited using spreadsheet software like Microsoft Excel, Google Sheets, or open-source alternatives like LibreOffice Calc.

CSV files are popular because they are easy to create, read, and process. They are often used for data exchange between different applications, importing and exporting data from databases, and as a simple format for storing structured data.

Here’s an example of a CSV file with three columns (Name, Age, City) and four rows of data:

Matplotlib histogram from CSV

To learn more about Matplotlib visualization go to matplotlib.org.

Seaborn

Seaborn is a statistical data visualization library built on top of Matplotlib. It provides a high-level interface for creating informative and attractive visualizations. Here’s an example of visualizing data from a CSV file using Seaborn Seaborn scatter plot from CSV

To learn more about Seaborn visualization go to seaborn.pydata.org.

Plotly

Plotly is an interactive plotting library that generates visually appealing and interactive plots. It can be used to visualize data from CSV files as well. Here’s an example using Plotly Express

Plotly histogram from CSV

To learn more about Plotly visualization go to plotly.com.

We’ve only scratched the surface of the visualization capabilities offered by Pandas, Matplotlib, Seaborn, and Plotly. Each library boasts numerous features, customizable options, and a wide array of plot types, enabling users to create powerful and sophisticated visualizations. Our discussion provided a glimpse of their potential, but further exploration of these libraries would reveal even more possibilities for effectively analyzing and presenting complex datasets.

Conclusion

We explored the importance of being able to work with CSV files using Python, particularly in the context of data analysis, visualization, and processing. We highlighted the versatility of Python and its extensive ecosystem of libraries as reasons for its popularity in handling CSV data.

We then discussed three different approaches to visualize CSV data in Python using popular libraries: Pandas with Matplotlib, Seaborn, and Plotly. Each library offers unique features and advantages, catering to different preferences and requirements in data visualization. Pandas was used consistently in all examples to read and process the data from the CSV files, demonstrating their effectiveness in handling data before visualizing it with other libraries.

In conclusion, visualizing CSV data using Python is a valuable skill that enables users to analyze, process, and communicate insights from structured data. The flexibility and power of Python’s libraries, such as Pandas, Matplotlib, Seaborn, and Plotly, make it an ideal choice for working with CSV files and creating meaningful visualizations.

Further reading

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Mastering Iteration: An In-Depth Guide to Looping Techniques in Rust

When it comes to programming, iteration or looping is a fundamental building block. Just like many other languages, Rust offers a variety of ways to do loops. In this article, we will dive deep into the various methods of looping in Rust, exploring for loops, while loops, and the loop

Three Ways Compute Cross Product in Python

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Use Pandas to Calculate Stats from an Imported CSV file

Pandas is a powerful Python package that can be used to perform statistical analysis. In this guide, you’ll see how to use Pandas to calculate stats from an imported CSV file.

The Example

To demonstrate how to calculate stats from an imported CSV file, let’s review a simple example with the following dataset:

person	salary	country
A	40000	USA
B	32000	Brazil
C	45000	Italy
D	54000	USA
E	72000	USA
F	62000	Brazil
G	92000	Italy
H	55000	USA
I	35000	Italy
J	48000	Brazil

Steps to Calculate Stats from an Imported CSV File

Step 1: Copy the Dataset into a CSV file

To begin, you’ll need to copy the above dataset into a CSV file. Then rename the CSV file as stats.

Step 2: Import the CSV File into Python

Next, you’ll need to import the CSV file into Python using this template:

import pandas as pd df = pd.read_csv(r'Path where the CSV file is stored\File name.csv') print(df)

Here is an example of a path where the CSV file is stored:

So the complete code to import the stats CSV file is captured below (note that you’ll need to modify the path to reflect the location where the CSV file is stored on your computer):

import pandas as pd df = pd.read_csv(r'C:\Users\Ron\Desktop\stats.csv') print(df)

Once you run the code in Python (adjusted to your path), you’ll get the following DataFrame:

 person salary country 0 A 40000 USA 1 B 32000 Brazil 2 C 45000 Italy 3 D 54000 USA 4 E 72000 USA 5 F 62000 Brazil 6 G 92000 Italy 7 H 55000 USA 8 I 35000 Italy 9 J 48000 Brazil 

Step 3: Use Pandas to Calculate Stats from an Imported CSV File

For the final step, the goal is to calculate the following statistics using the Pandas package:

• Mean salary
• Total sum of salaries
• Maximum salary
• Minimum salary
• Count of salaries
• Median salary
• Standard deviation of salaries
• Variance of of salaries

In addition, we’ll also do some grouping calculations:

• Sum of salaries, grouped by the Country column
• Count of salaries, grouped by the Country column

Once you’re ready, run the code to calculate the stats from the imported CSV file using Pandas. As indicated earlier, you’ll need to change the path to reflect the location where the CSV file is stored on your computer.

import pandas as pd df = pd.read_csv(r'C:\Users\Ron\Desktop\stats.csv') # block 1 - simple stats mean1 = df['salary'].mean() sum1 = df['salary'].sum() max1 = df['salary'].max() min1 = df['salary'].min() count1 = df['salary'].count() median1 = df['salary'].median() std1 = df['salary'].std() var1 = df['salary'].var() # block 2 - group by groupby_sum1 = df.groupby(['country']).sum() groupby_count1 = df.groupby(['country']).count() # print block 1 print('mean salary: ' + str(mean1)) print('sum of salaries: ' + str(sum1)) print('max salary: ' + str(max1)) print('min salary: ' + str(min1)) print('count of salaries: ' + str(count1)) print('median salary: ' + str(median1)) print('std of salaries: ' + str(std1)) print('var of salaries: ' + str(var1)) # print block 2 print('sum of values, grouped by the country: ' + str(groupby_sum1)) print('count of values, grouped by the country: ' + str(groupby_count1))

After you run the code in Python, you’ll get the following results:

mean salary: 53500.0 sum of salaries: 535000 max salary: 92000 min salary: 32000 count of salaries: 10 median salary: 51000.0 std of salaries: 18222.391598128816 var of salaries: 332055555.5555556 sum of values, grouped by the country: country Brazil 142000 Italy 172000 USA 221000 count of values, grouped by the country: country Brazil 3 3 Italy 3 3 USA 4 4 

You just saw how to calculate simple stats using Pandas. You may also want to check the Pandas Documentation to learn more about this library.

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