That’s definitely the synonym of “Python for data analysis”.
Pandas is a powerful data analysis Python library that is built on top of numpy which is yet another library that let’s you create 2d and even 3d arrays of data in Python. The pandas main object is called a dataframe. A dataframe is basically a 2d numpy array with rows and columns, that also has labels for columns and rows.
You can create dataframes out of various input data formats such as CSV, JSON, Python dictionaries, etc. Once you have the dataframe loaded in Python, you can apply various data analysis and visualization functions to the dataframe and basically turn the dataframe data into valuable information. See how easy it is to create a pandas dataframe out of this CSV file:
import pandas as pd df1=pd.read_csv("C:/PythonHow/Income_data.csv") print(df1)
This 3-line code would generate this output in Python:
That’s how it looks on a basic Python shell. If you want a fancier look of the dataframe, you would want to use the iPython notebook to write and run your Python code. You will learn how to set up and use the iPython notebook in the next lesson of this tutorial, but for now let’s just see how the iPython dataframe output would look:
Again, let’s focus on the code for now. What we basically did is we imported the pandas dataframe and assigned the pd namespace to it for the sake of code abbreviation. Then we used the read_csv method of the pandas library to read a local CSV file as a dataframe. Lastly, we printed out the dataframe. If you want to understand how read_csv works, do some code introspection:
This will print out the help string for the read_csv method. Note that the header parameter was set to True by default. That means the method automatically detects and assigns the first row of the CSV file as the pandas dataframe header. If you didn’t have a header in your csv data, you would want to set the header parameter to None explicitly:
That’s no brainier that having a header is a good idea. You can refer to your columns when you want to extract specific columns of the dataframe or even portions of rows and columns.
You will learn how to slice your dataframe and calculate means of those slices in the next lesson.