Top quick wins to boost your data analysis using Python

Python is a general-purpose programming language that is becoming an increasingly popular tool for data analysis. Its simplicity allows quick learning, so many data scientists choose Python for their professional needs.

With the average national salary of Python developers being $92,000, more and more people are interested in learning this programming language.

Moreover, the number of software libraries have reached maturity, allowing users of the popular statistical software packages like Stata and R to take advantage of the performance and flexibility of Python without any functionality sacrifices.

If you’re looking to increase the efficiency of your data analysis in Python, you can use the following quick wins.

1. Accumulate Resources

If you’re just beginning to learn data analysis in Python, you need to learn about it as much as you can. For that, you may turn to online resources. Thankfully, there are lots of useful resources that will get you up and running, including books, tools, tutorials, and interactive courses.

So to save you some time with finding resources on data analysis in Python, we have gathered this list of nice free resources.

  • Intro to Python for Data Science – a free interactive tutorial from a reliable online educational platform DataCamp that presents the essentials of data analysis in Python.
  • Introduction to Python for Data Science – this online course was created by professionals from Microsoft for anyone looking to learn data analysis with Python.
  • Bite Python – a weekly newsletter about everything Python. Just enter your email and receive the latest Python tips, tutorials, and news for free.

2. Convert Data to int Type in Pandas

Pandas is a software library written specifically for Python, so it’s safe to assume that you will use it to manipulate data. This quick win improves your data analysis by making it easier to make int-type data.

There is an easy way to convert data to this type. “Most commonly, programmers use .astype(‘int’), but the conversion may fail if there are some errors,” says Charlie David, a programmer. “I’ve learned to avoid it by using an alternative:


This command ensures conversion even if errors are detected.”

3. Find Unique Sets of Values among Millions of Entries

Let’s suppose you have a large set of data that you need to analyze. For example, there is a column with tens of thousands, hundreds of thousands, or even millions of unique entries, and your task is to identify a set of particular values using that data.

Many people use df.column_name.drop_duplicates(keep=”first”, inplace=False) to achieve this task, but you should also know that df.column_name.unique() does the same much quicker.

In case the final set of values contains a lot of duplicates, you can use keep=”first” option to remove them and make analysis easier.

4. Split a Column Using a Function

Use this simple split function to separate one column into multiple columns:

In [13]: df[‘column_name’] = df[‘column_name’].str.split(” “, expand=True)

5. Combine Two Existing Columns

If you need to combine two columns of data in Pandas into a single one, you can use the following options:


6. Quickly Group Columns by using GroupBy

With this function and value_counts, you can group by one column and count the values.


7. Increase Efficiency with diff

If your task is to determine the differences between certain values in the data, you can use a special function. For example, if you have a long list of people who spent various amounts of time at work, you can calculate who was in the office the longest by applying the following:

  • Groupby the person’s name
  • Calculate the time difference using diff():

df = df.sort_values(by=[‘name’,’timestamp’])

df[‘time_diff’] = df.groupby(‘name’)[‘timestamp’].diff()

  • You can also calculate the time per row by applying this code:

df[‘row_duration’] = df.time_diff.shift(-1)

7. Pull in Third-Party Data into Python

Let’s suppose that you’re a financial analyst and want to read data from sources like Google Finance and Yahoo! Finance (read about this Yahoo fetching issue here before you start). To get the data, you need to install the package called pandas-datareader via pip:

pip install pandas-datareader

Installing the package provides a wide range of options to pull in data into Python.


Python has seen an extraordinary growth in the recent years, with more and more world class companies using and supporting this programming language.

If you’re among those contributing to this growth, hope these quick wins will help you to increase the efficiency of your day-to-day data analysis tasks. Happy analyzing!

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