Do you need to know python to use pandas?
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Last Updated: August 9, 2022 What is Pandas in Python?Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. It is built on top of another package named Numpy, which provides support for multi-dimensional arrays. As one of the most popular data wrangling packages, Pandas works well with many other data science modules inside the Python ecosystem, and is typically included in every Python distribution, from those that come with your operating system to commercial vendor distributions like ActiveState’s ActivePython. What Can you Do with DataFrames using Pandas?Pandas makes it simple to do many of the time consuming, repetitive tasks associated with working with data, including:
In fact, with Pandas, you can do everything that makes world-leading data scientists vote Pandas as the best data analysis and manipulation tool available. With this series we will go through reading some data, analyzing it , manipulating it, and finally storing it. These are all things that you are able to be done with the Pandas library. There are many more functionalities that can be explored but that would simply take too much time and for people who are interested in the library and want to dive deeper into it the documentation for it is a great start: https://pandas.pydata.org/docs/user_guide/index.html#user-guide With deep roots in open source, and as a founding member of the Python Foundation, ActiveState actively contributes to the Python community. We offer the convenience, security and support that your enterprise needs while being compatible with the open source distribution of Python. Download ActiveState Python to get started or contact us to learn more about using ActiveState Python in your organization. Some BackgroundHello everyone! Today I want to write about the Pandas library (link to the website). Pandas stands for “Python Data Analysis Library”. According to the Wikipedia page on Pandas, “the name is derived from the term “panel data”, an econometrics term for multidimensional structured data sets.” But I think it’s just a cute name to a super-useful Python library! Pandas is quite a game changer when it comes to analyzing data with Python and it is one of the most preferred and widely used tools in data munging/wrangling if not THE most used one. Pandas is an open source, free to use (under a BSD license) and it was originally written by Wes McKinney (here’s a link to his GitHub page). What’s cool about Pandas is that it takes data (like a CSV or TSV file, or a SQL database) and creates a Python object with rows and columns called data frame that looks very similar to table in a statistical software (think Excel or SPSS for example. People who are familiar with R would see similarities to R too). This is so much easier to work with in comparison to working with lists and/or dictionaries through for loops or list comprehension (please feel free to check out one of my previous blog posts about very basic data analysis using Python. It would have been so much easier to do what I did there using Pandas!). Installation and Getting StartedIn order to “get” Pandas you would need to install it. You would also need to have Python 3.5.3 and above. as a pre-requirement for installation (will work with Python 3.6, 3.7, or 3.8) It is also dependent on other libraries (like NumPy) and has optional dependancies (like Matplotlib for plotting). Therefore, I think that the easiest way to get Pandas set up is to install it through a package like the Anaconda distribution , “a cross platform distribution for data analysis and scientific computing.” There you can download the Windows, OS X and Linux versions. If you want to install in a different way, these are the full installation instructions. In order to use Pandas in your Python IDE (Integrated Development Environment) like Jupyter Notebook or Spyder (both of them come with Anaconda by default), you need to import the Pandas library first. Importing a library means loading it into the memory and then it’s there for you to work with. In order to import Pandas all you have to do is run the following code: import pandas as pd Usually you would add the second part (‘as pd’) so you can access Pandas with ‘pd.command’ instead of needing to write ‘pandas.command’ every time you need to use it. Also, you would import numpy as well, because it is very useful library for scientific computing with Python. Now Pandas is ready for use! Remember, you would need to do it every time you start a new Jupyter Notebook, Spyder file etc. Working with PandasLoading and Saving Data with PandasWhen you want to use Pandas for data analysis, you’ll usually use it in one of three different ways:
There are different commands to each of these options, but when you open a file, they would look like this: pd.read_filetype()As I mentioned before, there are different filetypes Pandas can work with, so you would replace “filetype” with the actual, well, filetype (like CSV). You would give the path, filename etc inside the parenthesis. Inside the parenthesis you can also pass different arguments that relate to how to open the file. There are numerous arguments and in order to know all you them, you would have to read the documentation (for example, the documentation for pd.read_csv() would contain all the arguments you can pass in this Pandas command). In order to convert a certain Python object (dictionary, lists etc) the basic command is: pd.DataFrame()Inside the parenthesis you would specify the object(s) you’re creating the data frame from. This command also has different arguments (clickable link). You can also save a data frame you’re working with/on to different kinds of files (like CSV, Excel, JSON and SQL tables). The general code for that is: df.to_filetype(filename)Viewing and Inspecting DataNow that you’ve loaded your data, it’s time to take a look. How does the data frame look? Running the name of the data frame would give you the entire table, but you can also get the first n rows with
Selection of DataOne of the
things that is so much easier in Pandas is selecting the data you want in comparison to selecting a value from a list or a dictionary. You can select a column ( Filter, Sort and GroupbyYou can use different conditions to filter columns. For example, It is possible to sort values
in a certain column in an ascending order using The last command in this section is groupby. It involves splitting the data into groups based on some criteria, applying a function to each group independently and combining the results into a data structure. Data CleaningData cleaning is a very important step in data analysis. For example, we always check for missing values in the data by running It is sometimes
necessary to replace values with different values. For example, Join/CombineThe last set of basic Pandas commands are for joining or combining data frames or rows/columns. The three commands are:
These are the very basic Pandas commands but I hope you can see how powerful Pandas can be for data analysis. This post is just the tip of the iceberg — after all, entire books can be (and have been) written about data analysis with Pandas. I also hope this post made you feel like taking a dataset and playing around with it using Pandas! :) As always, if you have any comments, notes, suggestions or questions, please don’t hesitate to write me! Thanks for reading :) I will end, naturally with a picture of cute pandas and with a question — which do you prefer, giant pandas or red pandas???  See you next time! P.S. If you liked this tutorial, please check out my quick introduction to NumPy! Is Pandas hard to learn Python?Pandas is Powerful but Difficult to use
Pandas is the most popular Python library for doing data analysis. While it does offer quite a lot of functionality, it is also regarded as a fairly difficult library to learn well.
How much Python is required for Pandas?In order to “get” Pandas you would need to install it. You would also need to have Python 3.5. 3 and above. as a pre-requirement for installation (will work with Python 3.6, 3.7, or 3.8) It is also dependent on other libraries (like NumPy) and has optional dependancies (like Matplotlib for plotting).
Is Pandas and Python same?Pandas is an open source Python package that is most widely used for data science/data analysis and machine learning tasks. It is built on top of another package named Numpy, which provides support for multi-dimensional arrays.
Do I need to know Numpy before Pandas?You don't have to import numpy. Numpy and pandas are two different packages. They are both powerful libraries to edit data efficiently, and they are working together pretty good. This is why people use them together.
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