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1. Python Library – Pandas
It is a most famous Python package for data science, which offers
powerful and flexible data structures that make data analysis and
manipulation easy. Pandas make data importing and data analyzing
much easier. Pandas build on packages like NumPy and matplotlib
to give us a single & convenient place for data analysis and
visualization work. Pandas is a high level data manipulation tool
developed by Wes McKinney.

2. SYLLABUS 2022-23

3. Basic Features of Panda
1. With a pandas dataframe, we can have different data types (float, int, string,
datetime, etc) all in one place
2. Columns from a Panda data structure can be deleted or inserted
3. Good IO capabilities; Easily pull data from a MySQL database directly into a
data frame
4. Itsupportsgroupbyoperationfordataaggregationandtransformationandalowshigh
performancemergingandjoiningofdata.
5. It can easily select subsets of data from bulky datasets and even combine
multiple data sets together.
6. It has the functionality to find and fill missing data.
7. Reshaping and pivoting of data sets into different forms.
8. Label-based slicing, indexing and subsetting of large data sets.
9. It allows us to apply operations to independent groups within the data.
10. It supports advanced time-series functionality( which is the use of a model
to predict future values based on previously observed values.
11. It supports visualization by integrating libraries such as matplotlib, ans
seaborn etc. Pandas is best at handling hugs tabular datasets comprising
different data formats.

4. Data Structures in Pandas
A data structure is a way of storing and organizing data in a computer so that it can be
accessed and worked with in an appropriate way.
Important data structures of pandas are–Series,DataFrame, Panel
1. Series
Series is like a one-dimensional array like structure with homogeneous data. For example,
the following series is a collection of integers.
Basic feature of series are
 Homogeneous data
 Size Immutable
 Values of Data Mutable

5. Pandas Series
It is like one-dimensional array capable of holding data of any type (integer, string, float, python objects,
etc.). Series can be created using Series() method. Any list or dictionary data can be converted into series
using this method.
A series can be described as an ordered dictionary with mapping of index values to data values.
Create an Empty Series
s1--------
series variable
pd-------
alternate name given to Pandas module
import pandas as pd
s1 = pd.Series()
print(s1)
Output
Series([], dtype: float64)
Creating Series using Series() with arguments
Syntax :- pandas.Series( data, index, dtype, copy)
Data supplied to Series() can be
 A sequence( list)
 An ndarray
 A scalar value
 A python dictionary
 A mathematical expression or function

6. Creating Series using List
Like array, a list is also a one-dimensional datatype. But the difference lies in the fact that an array contains elements of
same datatype, while a list may contain elements of same or different data types.
Syntax :- pandas.Series( data, index = idx)
import pandas as pd
s1=pd.Series([10,20,30,40,50])
print(s1)
*Pandas create a default index and automatically assigns the index value from 0 to 4, which is length of the list-1
import pandas as pd
s1=pd.Series([10,20,30,40])
s1.index=['a', 'b', 'c', 'd']
print(s1)
(or)
import pandas as pd
s1=pd.Series([10,20,30,40], index= ['a', 'b', 'c', 'd'])
print(s1)

10. Creating Series using nd array

11. import pandas as pd
s1=pd.Series([1,2,3.3,4,7])
print(s1)
* One of the element in the list, is a float value, it will convert the rest of the integer values into float and displays a
float series.
range() method
import pandas as pd
s1=pd.Series(range(4))
print(s1)
Access single and multiple values based on index.
import pandas as pd
s1=pd.Series([2,3,5.3,7,9], index=['first','sec','third','fourth','fifth'])
print(s1['sec'])
Output
3.0
import pandas as pd
s1=pd.Series([2,3,5.3,7,9], index=['first','sec','third','fourth','fifth'])
print(s1)
print(s1[['sec','third','fifth']])

12. Values and index
import pandas as pd
s1=pd.Series([10,20,30,40,50],index=['First', 'sec', 'third', 'forth', 'fifth'])
print(s1.values)
import pandas as pd
s1=pd.Series([10,20,30,40,50],index=['First', 'sec', 'third', 'forth','fifth'])
print(s1.index)
Accessing data from a Series with Position
Indexing, slicing and accessing data from a series
import pandas as pd
s1=pd.Series([1,2,3,4,5], index=['a', 'b', 'c', 'd', 'e'])
print(s1[0])
print(s1[:3])
print(s1[-3:])

13. iloc and loc
iloc – used for indexing or selecting based on position ie, by row number and column number. It refers to position-
based indexing.
Syntax
iloc = [<row number range>,<column number range>]
It refers to name-based
loc - used for indexing or selecting based on name ie, by row name and column name.
indexing.
Syntax
iloc = [<list of row names >,<list of column names>]
import pandas as pd
s1=pd.Series([1,2,3,4,5], index=['a', 'b', 'c', 'd', 'e'])
print(s1.iloc[1:4])
print(s1.loc['b':'e'])

14. Retrieving values from Series using head()and tail () functions
Series.head() function in a series fetches first ‘n’ values from a pandas object. By default, it gives us the top 5 rows of data
in the series. Series.tail() function displays the last 5 elements by default.
import pandas as pd
s1=pd.Series([10,20,30,40,50,60,70,80,90])
print(s1.head())
import pandas as pd
s1=pd.Series([10,20,30,40,50,60,70,80,90])
print(s1.head(3))
import pandas as pd
s1=pd.Series([10,20,30,40,50,60,70,80,90])
print(s1.tail(2))
Creating a Series from Scalar or Constant Values
A data is a scalar value for which it is a must to provide an index. This constant value shall be repeated to match the
length of the index.
import pandas as pd
s1=pd.Series(55, index=['a', 'b', 'c', 'd', 'e'])
print(s1)
Note :- here 55 is repeated for 5 times (as per no of index)

15. import pandas as pd
s1=pd.Series(55, index=[1,2,3,4,5])
print(s1)
Using range() method
import pandas as pd
s1=pd.Series(40, index=range(0,4))
print(s1)
import pandas as pd
s1=pd.Series(40, index=range(1,6,2))
print(s1)
Creating a Series with index of String (text) type
String or text can be used as an index to the elements of a series.
import pandas as pd
s1=pd.Series('Stay Home', index=['Madhav', 'Smitha', 'Vivek'])
print(s1)

16. Creating a Series with range() and for loop
import pandas as pd
s1=pd.Series(range(1,15,3), index=[x for x in 'abcde'])
print(s1)
Creating a Series using two different lists
* Two lists are passed as arguments to Series()method
import pandas as pd
months=['jan', 'feb', 'mar', 'apr', 'may']
no_days=[31,28,31,30,31]
s1=pd.Series(no_days,index=months)
print(s1)
Creating a Series using missing values [NaN]
We may need to create a series object for which size is defined but some element or data are missing. This is handled
by defining NaN [Not a Number], which is an attribute of Numpy library, defining a missing value using np.NaN.
import pandas as pd
import numpy as np
s1=pd.Series([31,28,31,np.NaN,31])
print(s1)

17. Creating Series from Dictionary
Using dictionary for creating a series gives us the advantage of built-in keys used as index. We do not require declaring
an index as a separate list: instead, built-in keys will be treated as the index
import pandas as pd
data = {'a' : 0., 'b' : 1., 'c' : 2.}
s = pd.Series(data)
print(s)
* A dictionary can be passed as input and if no index is specified, the dictionary keys are taken in the
sorted order to construct index
import pandas as pd1
data = {'a' : 0., 'b' : 1., 'c' : 2.}
s = pd1.Series(data,index=['b','c','d','a'])
print(s)
import pandas as pd
s1=pd.Series({'Jan':31,'Feb':28,'Mar':31,'Apr':30})
print(s1)

18. Naming a series
We can give a name to the two columns, index and values of a series using ‘name’ property.
import pandas as pd
s=pd.Series({'Jan':31,'Feb':28,'Mar':31,'Apr':30})
#naming the series and index
s.name='Days'
s.index.name='Month'
print(s)
* The index column is assigned the name ‘Month’ and data is assigned the name ‘Days’
Creating a Series using a mathematical expression/function
import pandas as pd
import numpy as np
s1=np.arange(5,10)
print(s1)
s2=pd.Series(index=s1,data=s1*4)
print(s2)

19. import pandas as pd
import numpy as np
s1=np.arange(5,10)
print(s1)
s2=pd.Series(index=s1,data=s1**4)
print(s2)
Mathematical operation on series
import pandas as pd
s1=pd.Series([10,20,30,40,50], index=[1,2,3,4,5])
print(s1)
s2=pd.Series([15,25,35,45,55], index=[1,2,3,4,5])
print(s2)
s3=pd.Series([11,22,33,44,55], index=[10,20,30,40,50])
print(s3)
print(s1+s2)
print(s1*s2)
print(s2/s1)
print(s1+s3)

20. Vector operations on series
Series supports vectors operations. Any operation to be performed on a series
gets performed on every single element of it.
import pandas as pd
s1=pd.Series([10,20,30,40,50], index=[1,2,3,4,5])
print(s1>25) # returns booleanoutput
import pandas as pd
s1=pd.Series([10,20,30,40,50], index=[1,2,3,4,5])
print(s1[s1>25]) # print s1 only if the value of s1 is greater than 25
Modifying Elements of a Series Object
import pandas as pd
s1=pd.Series([10,20,30,40,50], index=[1,2,3,4,5])
s2=pd.Series([15,25,35,45,55], index=[1,2,3,4,5])
s1[2]=222
s2[1:4]=[1000,2000,3000]
print(s1)
print(s2)

21. Deleting elements from a Series
We can delete an element from a series using drop() method by passing
the index of the element to be deleted as the argument to it.
import pandas as pd
s1=pd.Series([10,20,30,40,50], index=[1,2,3,4,5])
print(s1.drop(3))
import pandas as pd
s1=pd.Series([10,20,30,40,50], index=[1,2,3,4,5])
s2=pd.Series([[15,25,34],[35,45,55]])
print(s1)
print(s2)
print(s1.dtype)
print(s2.dtype)
print(type(s1))
print(type(s2))
print(s1.shape)
print(s2.shape)
print(s1.ndim, ' ', s2.ndim)
print(s1.size,'; ',s2.size)
print(s1.empty)
print(s2.hasnans)
print(s2.count())
print(s1.nbytes,';',s2.nbytes)

22. Series Object Attributes
Attributes Description
Series.index Returns index of the series
Series.values Returns ndarrays
Series.dtype Returns dtype object of the underlying data
Series.shape Returns tuple of the shape of underlying data
Series.size Returns the size of the element
Series.itemsize Returns the size of the dtype
Series.hasnans Returns true if there are any NaN
Series.empty Returns true if series object is empty