Introduction to a Python Libraries and python frameworks

INTRODUCTION TO PYTHON
LIBRARIES
--Savithri S

WHAT IS PYTHON LIBRARY?
 A library is a collection of code that makes everyday tasks more
efficient.
 A Python library is a collection of related modules.
 NumPy, Pandas and Matplotlib are three well-established Python
libraries for scientific and analytical use.
 These libraries allow us to manipulate, transform and visualise data
easily and efficiently.

WHAT IS PANDAS?
 Pandas is a Python library used for working with data sets.
 It has functions for analyzing, cleaning, exploring, and manipulating data.
 The name 'Pandas' comes from the econometrics term 'panel data' describing data sets that
include observations over multiple time periods.
Installing Pandas :
pip install pandas
Import Pandas
 import pandas
Data Structure in Pandas
A data structure is a collection of data values and operations that can be applied to that data.
It enables efficient storage, retrieval and modification to the data.

TWO COMMONLY USED DATA STRUCTURES IN
PANDAS
• Series
• DataFrame

WHAT IS A SERIES?
 A Pandas Series is like a column in a table.
 It is a one-dimensional array holding data of any type.
import pandas as pd
a = [1,7,2]
myvar = pd.Series(a)
print(myvar)

O/P:
 Labels
 If nothing else is specified, the values are labeled with their index number. First value has index 0, second value has index 1 etc.
0 1
1 7
2 2
dtype: int64

Create Labels
With the index argument, you can name your own labels.
Example
Create your own labels:
import pandas as pd
a = [1, 7, 2]
myvar = pd.Series(a, index = ["x", "y", "z"])
print(myvar)
O/P
x 1
y 7
z 2
dtype: int64

CREATION OF SERIES FROM NUMPY ARRAYS
 We can create a series from a one-dimensional (1D)
 NumPy array, as shown below:
import numpy as np # import NumPy with alias np
import pandas as pd
array1 = np.array([1,2,3,4])
series3 = pd.Series(array1)
print(series3)

CREATION OF SERIES FROM DICTIONARY
dict1 = {'India': 'NewDelhi', 'UK':'London', 'Japan': 'Tokyo'}
print(dict1) #Display the dictionary
series8 = pd.Series(dict1)
print(series8) #Display the series
O/P:
 India NewDelhi
 UK London
 Japan Tokyo
 dtype: object

ACCESSING ELEMENTS OF A SERIES
 Indexing
Indexing in Series is similar to that for NumPy arrays,and is used to access
elements in a series.
Indexes are of two types: positional index and labelled index.
 Positional index takes an integer value that corresponds to its position in
the series starting from 0, whereas labelled index takes any user-defined
label as index.

POSITIONAL INDEX AND LABELLED INDEX
Positional index
seriesNum = pd.Series([10,20,30])
seriesNum[2]
O/P: 30
Labelled index
seriesMnths = pd.Series([2,3,4],index=["Feb","Mar","Apr"])
seriesMnths["Mar"]
O/P:3

SLICING
Slicing
Sometimes, we may need to extract a part of a series. We can define which
part of the series is to be sliced by specifying the start and end parameters
[start :end] with the series name.
When we use positional indices for slicing, the value at the end index
position is excluded, i.e., only (end -start) number of data values of the
series are extracted.
seriesCapCntry = pd.Series(['NewDelhi', 'WashingtonDC', 'London',
 'Paris'], index=['India', 'USA', 'UK', 'France'])
seriesCapCntry[1:3] #excludes the value at index position 3
USA WashingtonDC
UK London
dtype: object

 If labelled indexes are used for slicing, then value at the end index
 label is also included in the output, for example:
seriesCapCntry['USA' : 'France']
 USA WashingtonDC
 UK London
 France Paris
 dtype: object

 We can also get the series in reverse order, for
 example:
seriesCapCntry[ : : -1]
 France Paris
 UK London
 USA WashingtonDC
 India NewDelhi
 dtype: object

WHAT IS A DATAFRAME?
 A DataFrame is a data structure that organizes data into a 2-
dimensional table of rows and columns, much like a spreadsheet.
DataFrames are one of the most common data structures used in
modern data analytics because they are a flexible and intuitive way
of storing and working with data.

 The Matplotlib library in Python is used for plotting graphs and visualisation.
Using Matplotlib, with just a few lines of code we can generate publication
quality plots, histograms, bar charts, scatterplots, etc.
 1. A Numpy array requires homogeneous data, while a Pandas DataFrame can
have different data types (float, int, string, datetime, etc.).
 2. Pandas have a simpler interface for operations like file loading, plotting,
selection, joining, GROUP BY, which come very handy in data-processing
applications.
 3. Pandas DataFrames (with column names) make it very easy to keep track of
data.
 4. Pandas is used when data is in Tabular Format, whereas Numpy is used for
numeric array based data manipulation.

Introduction to a Python Libraries and python frameworks

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