Introduction

This notebook is a simple mini-tutorial to introduce you to basic functions of Jupyter, Python, Pandas and matplotlib with the aim of analyzing software data. Therefore, the example is chosen in such a way that we come across the typical methods in a data analysis. Have fun!

The Jupyter Notebook System

First, we’ll take a closer look at Jupyter Notebook. What you see here is Jupyter, the interactive notebook environment for programming. We see below a cell in which we can enter Python code. Let’s just type in a string called "Hello World" here. With the key combination Ctrl + Enter we can execute this cell.

The result is immediately visible under the cell. Let’s create another cell! This works by pressing the ESC key followed by the letter b. Alternatively, at the end of a notebook, we can run a cell with Shift + Enter and create a new cell right away.

Here we see an important feature of Jupyter: The distinction between command mode (accessible via the Esc key) and input mode (accessible via the Enter key). In command mode, the border of the current cell is blue. In input mode, the border turns green. Let’s go to the command mode and press m. This changes the cell type to a markdown cell. Markdown is a simple markup language that can be used to write and format texts. This allows us to directly document the steps we have taken.

A quick Introduction to Python

Let’s take a look at some basic Python programming constructs that we will need later when working with Pandas.

First, we save our text “Hello World” into to a variable called text by assigning it with the = symbol. We write text again in the row below, execute the cell and see the result displayed under the cell.

By using the array notation with the square brackets [ and ], we can access the first letter of our text with a 0-based index (this also works for other types like lists).

We can output the last letter with a [-1] as selector because negative numbers in parentheses represent the indexing from behind.

We can also work with so-called “slices”. This allows us to output any range of values from the content in our text variable.

The following slice [:-1] is an abbreviation for a 0-based slice [0:-1].

Finally, we take a look at how to reverse a text (or even a list). This works with the :: notation and the specification of -1.

We can explore the further functionality of a library (or an object stored in a variable) by looking at the methods and attributes of a class or object. When we write the beginning of a command, like text. in our string example, we can use the integrated autocompletion of Jupyter with the tab key Tab to see which methods the currently used object offers. We can then select a method that we want with the arrow key down or narrowing our search by pressing e.g. the first letters of upper. If we press Enter on the selected item and then Shift+ Tab, the signature of the corresponding functionality and the section of the help documentation appears. Pressing Shift + Tab twice will display the full help. So in our case, by calling upper() on our text variable, we can have our text written in capital letters.

The interactive source code documentation also helps us to find out which arguments we can add in a method in addition to the obligate parameters. This can be easily observed when using the split method on our text variable and the integrated help functionality.

Git history analysis

OK, let’s start the analysis!

In this notebook, we want to take a closer look at the development history of the open source project “Linux” based on the history of the corresponding GitHub mirror repository.

A local clone of the GitHub repository https://github.com/torvalds/linux/ was created by using the command

git clone https://github.com/torvalds/linux.git

The relevant parts of the history for this analysis were produced by using

git log --pretty="%ad,%aN" --no-merges > git_demo_timestamp_linux.csv

This command returned the commit timestamp (%ad) and the author name (%aN) for each commit of the Git repository. The corresponding values are separated by commas. We also indicated that we do not want to receive merge commits (via --no-merges). The result of the output was saved in the file git_demo_timestamp_linux.csv.

Note: For an optimized demo, headers and the separator has been changed manually in the provided dataset to get through this analysis more easily. The differences can be seen at https://www.feststelltaste.de/developers-habits-linux-edition/, which was done with the original dataset.

Getting to know Pandas

Pandas is a data analysis tool written in Python (and partly in C), which is ideally suited for the evaluation of tabular data due to the use of effective data structures and built-in statistics functions.

Basics

We import the data from above with the help of Pandas. We import pandas with the common abbreviation pd using the import... as.. syntax of Python.

We can check whether the import of the module really worked by checking the documentation of the pd module. To do this, we append the ? operator to the pd variable and execute the cell. The documentation of the module appears in the lower part of the browser window. We can read through this area and make it disappear again with the ESC key.

We read the compressed CSV file git_demo_timestamp_linux.gz with the read_csv() method and the parameter URL which defines the location of the file. Since the file is a gzip-packed file and we get this file over the web, we have to specify the used compression algorithm using compression='gzip'.

The result of our execution is stored in the variable git_log. We’ve just loaded data into a so-called DataFrame (something like a programmable Excel worksheet), which in our case consists of two Series (= columns).

We can now perform operations on the DataFrame. For example, we can use head() to display the first five entries.

Next, we call info() on the DataFrame to get some basic data about the read in data.

We can access the individual Series / columns by using the ['<column name>'] or (in most cases, i.e. as long as the column names do not overlap with the method name offered by the DataFrame itself) by directly using the name of the Series.

First Analysis

We can also perform various operations on a Series. For example, with value_counts(), we can count the values contained in a Series and let them sort according to their frequency. The result is again a Series, but this time with the totaled and sorted values. We can additionally call head(10) on this Series. This gives us a quick way to display the TOP-10 values of a Series. We can then record the result in a variable top10 and output it by writing the variable to the next cell row.

First visualizations

Next, we want to visualize or plot the result. To display the plotting result of the internally used plotting library matplotlib directly in the notebook, we have to execute this magic command in our notebook

%matplotlib inline

before calling the plot() method.

By default, when plot() is called on a DataFrame or Series, a line chart is created.

That doesn’t make much sense here, so we use a sub-method of plot called bar() to create a bar chart.

This data can also be visualized as a pie chart. For this, we call the pie() method instead of bar(). We can also add a semicolon ; after the plot command to avoid printing the text of the reference.

However, the diagram does not look very nice here.

With the optional styling parameters, we can achieve that we get a nicer graphics. We use

• figsize=[7,7] as size
• title="Top 10 authors" as title
• labels=None to avoid displaying the superfluous label on the left.

Working with dates

Now let’s look at the timestamp information. We want to find out at what time of day the developers commit.

Before we can enter the world of time series processing, we must first convert our column with the dates into the appropriate data type. At the moment our column timestamp is still a string, i.e. of textual nature. We can see this by using the helper function type(<object>) to display the first entry of the timestamp column:

Of course, Pandas also helps us to convert data types. The function pd.to_datetime takes as first parameter a series with dates and converts them. The return value is a Series with values of the data type Timestamp. The conversion works for most textual dates mostly automagically [sic!], because Pandas can handle different date formats. We also write the result back into the same column.

To check if the conversion was successful, we can check the first value of our converted column timestamp_local by calling type() again.

We can now also access individual parts of the date values. For this, we use the dt (“datetime”) object with its properties like hour.

Together with the value_counts() method that I’ve already introduced above, we can now count values again after their occurrence. However, it is important that we also set the parameter sort=False to avoid sorting according to keep the order of the hours.

We can display the result by means of a bar chart and thus get an overview of how many commits occured for each hour.

We now additionally label the plot. To do this, we store the return object of the bar() function in the variable ax. This is an Axes object of the underlying plotting library matplotlib, through which we can customize additional properties of the plot. We set here

• the title via set_title("<titel name>")
• the label of the X-axis with set_xlabel("<X-axis label>")
• the label of the Y-axis with set_ylabel<"Y-axis label>")

The result is a more meaningful, labeled bar chart.

We can also analyze the commits per weekdays. To do this, we use the weekday attribute of the datetime attribute dt. The values here are 0-based with Monday as the first day of the week. As usual, we count the values using value_counts and do not sort the values by size but keep the sorting by weekday.

The result in commits_per_weekday can be output as a bar chart using plot.bar().

Displaying the commit history

In the following, we want to see the progress of the number of commits over the last years by using a DatetimeIndex based DataFrame. To do this, we set the timestamp column as index using set_index('<columnname>'). Furthermore, we select just the author column. Thus we work continuously on a pure Series instead of a DataFrame.

Side note: The usage of a Series is almost similar to a DataFrame with regard to the statistical functions. However, a Series is not beautifully formatted in a table, which is why I personally prefer using a DataFrame.

Using the resample("<time unit>") function of the DataFrame, we can now group values according to certain time units such as days (D), months (M), quarters (Q) or years (A). We use a resample("D") for counting per day. We also specify how the individual values should be combined per time unit. For this, we select the count() function to count the number of commits for each day.

To show the commit history over the years, we calculate the cumulative sum of all daily entries using cumsum(). All values get summed up one after the other.

The result is plotted as a line diagram and the number of commits is shown over the years.

The End

OK, that’s it for now!

You’ve learned some basics about Pandas and its usage with software data. This will hopefully get you started in your daily work. Other important topics that are still missing are:

• Reading in complicated or semi-structured data structures
• Merging different data sources with pd.merge and join
• Grouping similar data using groupby
• Transforming data with pivot_table

But with the features shown, you are on a good way to become a Software Development Analyst that can leverage Data Science to fix problems in software systems!

If you want to dive deeper into this topic, take a look at my other blog posts on that topic. I’m also offering training for companies who want to fix their problems using data analysis in software development.

I’m looking forward to your comments and feedback!