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CHAPTER-1 Data Handling using Pandas –I
Pandas:
• It is a package useful for data analysis and manipulation.
• Pandas provide an easy way to create, manipulate and wrangle the
data.
• Pandas provide powerful and easy-to-use data structures, as well
as the means to quickly perform operations on these structures.
Data scientists use Pandas for its following advantages:
• Easily handles missing data.
• It uses Series for one-dimensional data structure and DataFrame
for multi-dimensional data structure.
• It provides an efficient way to slice the data.
• It provides a flexible way to merge, concatenate or reshape the
data.
DATA STRUCTURE IN PANDAS
A data structure is a way to arrange the data in such a way that so it
can be accessed quickly and we can perform various operation on this
data like- retrieval, deletion, modification etc.
Pandas deals with 3 data structure-
1. Series
2. Data Frame
3. Panel
We are having only series and data frame in our syllabus.
CREATED BY: SACHIN BHARDWAJ PGT(CS) KV NO1 TEZPUR, VINOD VERMA PGT (CS) KV OEF KANPUR
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Series
Series-Series is a one-dimensional
DATAFEAME array like structure with
homogeneous data, which can be used to handle and manipulate data.
What makes it special is its index attribute, which has incredible
functionality and is heavily mutable.
It has two parts-
1. Data part (An array of actual data)
2. Associated index with data (associated array of indexes or data labels)
e.g.-
Index Data
0 10
1 15
2 18
3 22
✓ We can say that Series is a labeled one-dimensional array
which can hold any type of data.
✓ Data of Series is always mutable, means it can be changed.
✓ But the size of Data of Series is always immutable, means it
cannot be changed.
✓ Series may be considered as a Data Structure with two
arrays out which one array works as Index (Labels) and the
second array works as original Data.
✓ Row Labels in Series are called Index.
CREATED BY: SACHIN BHARDWAJ PGT(CS) KV NO1 TEZPUR, VINOD VERMA PGT (CS) KV OEF KANPUR
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Syntax to create a Series:
<Series Object>=pandas.Series (data, index=idx (optional))
✓ Where data may be python sequence (Lists), ndarray,
scalar value or a python dictionary.
How to create Series with nd array
DATAFEAME
Program-
import pandas as pd
Output-
import numpy as np Default Index
0 10
arr=np.array([10,15,18,22])
1 15
s = pd.Series(arr) 2 18
print(s) 3 22
Data
Here we create an
array of 4 values.
CREATED BY: SACHIN BHARDWAJ PGT(CS) KV NO1 TEZPUR, VINOD VERMA PGT (CS) KV OEF KANPUR
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How to create Series with Mutable index
DATAFEAME
Program-
import pandas as pd Output-
import numpy as np first a
arr=np.array(['a','b','c','d']) second b
third c
s=pd.Series(arr,
fourth d
index=['first','second','third','fourth'])
print(s)
CREATED BY: SACHIN BHARDWAJ PGT(CS) KV NO1 TEZPUR, VINOD VERMA PGT (CS) KV OEF KANPUR
CHAPTER-1 Data Handling using Pandas –I
Pandas:
• It is a package useful for data analysis and manipulation.
• Pandas provide an easy way to create, manipulate and wrangle the
data.
• Pandas provide powerful and easy-to-use data structures, as well
as the means to quickly perform operations on these structures.
Data scientists use Pandas for its following advantages:
• Easily handles missing data.
• It uses Series for one-dimensional data structure and DataFrame
for multi-dimensional data structure.
• It provides an efficient way to slice the data.
• It provides a flexible way to merge, concatenate or reshape the
data.
DATA STRUCTURE IN PANDAS
A data structure is a way to arrange the data in such a way that so it
can be accessed quickly and we can perform various operation on this
data like- retrieval, deletion, modification etc.
Pandas deals with 3 data structure-
1. Series
2. Data Frame
3. Panel
We are having only series and data frame in our syllabus.
CREATED BY: SACHIN BHARDWAJ PGT(CS) KV NO1 TEZPUR, VINOD VERMA PGT (CS) KV OEF KANPUR
, Visit Python4csip.com for more updates
Series
Series-Series is a one-dimensional
DATAFEAME array like structure with
homogeneous data, which can be used to handle and manipulate data.
What makes it special is its index attribute, which has incredible
functionality and is heavily mutable.
It has two parts-
1. Data part (An array of actual data)
2. Associated index with data (associated array of indexes or data labels)
e.g.-
Index Data
0 10
1 15
2 18
3 22
✓ We can say that Series is a labeled one-dimensional array
which can hold any type of data.
✓ Data of Series is always mutable, means it can be changed.
✓ But the size of Data of Series is always immutable, means it
cannot be changed.
✓ Series may be considered as a Data Structure with two
arrays out which one array works as Index (Labels) and the
second array works as original Data.
✓ Row Labels in Series are called Index.
CREATED BY: SACHIN BHARDWAJ PGT(CS) KV NO1 TEZPUR, VINOD VERMA PGT (CS) KV OEF KANPUR
, Visit Python4csip.com for more updates
Syntax to create a Series:
<Series Object>=pandas.Series (data, index=idx (optional))
✓ Where data may be python sequence (Lists), ndarray,
scalar value or a python dictionary.
How to create Series with nd array
DATAFEAME
Program-
import pandas as pd
Output-
import numpy as np Default Index
0 10
arr=np.array([10,15,18,22])
1 15
s = pd.Series(arr) 2 18
print(s) 3 22
Data
Here we create an
array of 4 values.
CREATED BY: SACHIN BHARDWAJ PGT(CS) KV NO1 TEZPUR, VINOD VERMA PGT (CS) KV OEF KANPUR
, Visit Python4csip.com for more updates
How to create Series with Mutable index
DATAFEAME
Program-
import pandas as pd Output-
import numpy as np first a
arr=np.array(['a','b','c','d']) second b
third c
s=pd.Series(arr,
fourth d
index=['first','second','third','fourth'])
print(s)
CREATED BY: SACHIN BHARDWAJ PGT(CS) KV NO1 TEZPUR, VINOD VERMA PGT (CS) KV OEF KANPUR
