Pandas is one of the most important Python packages, especially if you are a data analyst or data scientist. It offers amazing visualization tools that will not just help you get the attention of your audience, but will also help them understand your work faster. There are several uses of Pandas that you will come across in data analysis and beyond.
Through this library, you will learn how to analyze, transform and clean data, and present it in a manner that makes sense to your audience. Most people have data stored in Excel files. You can import this data to Pandas and convert it automatically into data frames. Data frames are simply tables, but with more privileges than the regular Excel tables.
From the data frames, you can perform statistical calculations and get answers to important questions about the data, like correlation analysis, media, max and min estimations for each column, or determine the distribution patterns for your data.
Many times, you come across data that is so jumbled up you need to spend more time cleaning it before you can make sense of it. Pandas allows you to clean such data by using specific criteria to filter the data, eliminating inaccurate data or missing values from your final data.
Beyond this, you can also use different features in Pandas to visualize your data and have your audience appreciate the appeal. You can do this through plot lines, bubbles, histograms, and bars from Matplotlib.
The data you are working on will always be useful in the future. For this reason, Pandas allows you to save the data once it has been cleaned and processed into an Excel sheet, or any other file system or database you prefer.
Pandas is not just an important library for data analysis. It is part of many other libraries that you will use from time to time. Knowledge of Pandas will help you in working with NumPy, performing statistical analytics in SciPy, working with machine learning algorithms in Scikit-learn, and using plotting functions in Matplotlib.
Before you get started with Pandas, you must have a working knowledge of Python. You do not necessarily need to be an expert at Python, but some credible knowledge will help you, especially about the basics like iterations, functions, dictionaries, and lists. Other than the fundamentals of Python, you should also learn a bit about NumPy because it shares a lot of similarities with Pandas.
Installing this library is straightforward. You will use your command line for Windows users, or Terminal if you are using a Mac as follows:
For Macs:
pip install pandas
For Windows:
conda install pandas
In case you are using a Jupyter notebook, you can install Pandas as follows:
!pip install pandas
Why is it important to use (!) in the notebook? It instructs your system to run the code as if you were using terminal or command line.
Through this library, you will learn how to analyze, transform and clean data, and present it in a manner that makes sense to your audience. Most people have data stored in Excel files. You can import this data to Pandas and convert it automatically into data frames. Data frames are simply tables, but with more privileges than the regular Excel tables.
From the data frames, you can perform statistical calculations and get answers to important questions about the data, like correlation analysis, media, max and min estimations for each column, or determine the distribution patterns for your data.
Many times, you come across data that is so jumbled up you need to spend more time cleaning it before you can make sense of it. Pandas allows you to clean such data by using specific criteria to filter the data, eliminating inaccurate data or missing values from your final data.
Beyond this, you can also use different features in Pandas to visualize your data and have your audience appreciate the appeal. You can do this through plot lines, bubbles, histograms, and bars from Matplotlib.
The data you are working on will always be useful in the future. For this reason, Pandas allows you to save the data once it has been cleaned and processed into an Excel sheet, or any other file system or database you prefer.
Pandas is not just an important library for data analysis. It is part of many other libraries that you will use from time to time. Knowledge of Pandas will help you in working with NumPy, performing statistical analytics in SciPy, working with machine learning algorithms in Scikit-learn, and using plotting functions in Matplotlib.
Before you get started with Pandas, you must have a working knowledge of Python. You do not necessarily need to be an expert at Python, but some credible knowledge will help you, especially about the basics like iterations, functions, dictionaries, and lists. Other than the fundamentals of Python, you should also learn a bit about NumPy because it shares a lot of similarities with Pandas.
Installing this library is straightforward. You will use your command line for Windows users, or Terminal if you are using a Mac as follows:
For Macs:
pip install pandas
For Windows:
conda install pandas
In case you are using a Jupyter notebook, you can install Pandas as follows:
!pip install pandas
Why is it important to use (!) in the notebook? It instructs your system to run the code as if you were using terminal or command line.
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