Pandas — Intro & Series

What it is? How to use it? — #PySeries#Episode 07

What is PANDAS?

1. Pandas is an open-source library build on top of NumPy;
2. Pandas allows for analysis and data cleaning and preparations;
3. Pandas excels in performance and productivity;
4. Pandas also has built-in visualization features;
5. Pandas can work with data from a wide variety of sources.

Fig 1. Pandas is a fast, powerful, flexible, and easy to use open-source data analysis and manipulation tool,
built on top of the Python NumPy programming language.

Data Science topics with Pandas:

Here are the topics for our study about Pandas:

.Series (this one:)
.DataFrames
.Missing Data
.GroupBy
.Merging, Joinning, and Concatenating
.Operations
.Data Input and Output

Fig 1. Numpy & Pandas Together!

The first topic will be the Series:

SERIES

We will need these four object in Python to open a Series in Pandas:

LIST
DATA
ARRAY
DICTIONARY

Get your Jupyter Notebook (or Google Colab) and type:

# Pandas is build on top of NumPy
import numpy as np

Pandas needs NumPy because it is built on top of it.

Now initialize Pandas itself like this:

# To start up PANDAS package
import pandas as pd

Let`s get down to code; Here is our List:

labels=['a','b','c']

And our other three separate Python Object: data, NumPy array, and a dictionary:

my_data=[10,20,30]
arr=np.array(my_data)
d={'a':10,'b':20, 'c':30}

HOW TO CREATE A SERIES

First, we’ll need theses four Python Object:

1. LIST
2. DATA
3. ARRAY and
4. DICTIONARY

Then we pass in our DATA to the Series Method:

# Series : It look a lot as np array, except that here 
# it’s very distinguish we have an INDEX and the actual DATA,
# that is, it is an indexed array:)
pd.Series(data=my_data)
0    10
1    20
2    30
dtype: int64

Or our DATA together w/ LIST (labels):

# Now I specify that INDEX is iguals to the LIST labels
# Now I have a label-INDEX Series
pd.Series(data=my_data, index=labels)
a    10
b    20
c    30
dtype: int64

In this very order: 1º DATA then 2º INDEX: Series (DATA, INDEX)

# You don’t need the constant-specifier 
# as long as you put them in the correct order
pd.Series(my_data, labels)
a    10
b    20
c    30
dtype: int64

Or finally, what’s really cool:

Pass in the DICTIONARY:

# Pandas takes the key as an INDEX, and the dictionary 
# values as our DATA.
# So that’s a nice and fast way to quickly create a SERIES!
pd.Series(d)
a    10
b    20
c    30
dtype: int64

A Series can hold pretty much almost any type of data object of Python as its data points and, more interesting than that is we can pass in built-in functions like sum(), print(), and len(), etc

It can even hold references of these functions as data points :)

pd.Series(data=[sum, print, len])

We probably never actually use this, but this demonstrates the flexibility of the PANDAS Series as far as holding different object types!

(Jose Portilla — Python For Data Science course)

Arithmetic with Series

Series (DATA, INDEX)

ser1=pd.Series([1,2,3,4], ['USA', 'USSR', 'GERMANY', 'JAPAN'])
ser1
USA        1
USSR       2
GERMANY    3
JAPAN      4
dtype: int64

Other Series follows:

ser2=pd.Series([1,2,5,4], ['USA','USSR', 'ITALY', 'JAPAN'])
ser2
USA      1
USSR     2
ITALY    5
JAPAN    4
dtype: int64

How to Recovery the Series:

# To rescue the data pass in the INDEX
ser1['USA']
1

Or pass in the INDEX like this:

ser1.USA
1

Arithmetic

# We can do arithmetic operations with the Series too:
ser1 + ser2
GERMANY    NaN
ITALY      NaN
JAPAN      8.0
USA        2.0
USSR       4.0
dtype: float64

That’s it for Pandas Series!

In the next episode let’s discuss DataFrame!

Stay tuned!

Bye, for now, o/

GitHub Repo link

Google Colab link [TODO: THE LINK FOR COLAB GOES HERE!]

Credits & References:

Jose Portilla — Python for Data Science and Machine Learning Bootcamp — Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

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