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Pandas is highly flexible, allowing you to work with:
• Tabular data with heterogeneously-typed columns, like in an SQL table or Excel spreadsheet.
• Ordered and unordered time series data, which may not necessarily be of fixed frequency.
• Arbitrary matrix data, either homogeneous or heterogeneous, with row and column labels.
• A wide range of observational or statistical datasets.
Installing Pandas
To install Pandas from command line, we need to type in:
pip install pandas
All libraries including NumPy and Pandas can be installed only when Python is already installed on that system.
Two commonly used data structures in Pandas are:
• Series
• DataFrames
Series
Series is a one-dimensional array that can store data of any type like integer, string, float, python objects, etc.
We can also say that the Pandas series is just like a column of a spreadsheet.
The values can be referred to by using data axis labels also called index. Indexes are of two types: positional
index and labelled index. Positional indexes are integers starting with default 0 whereas labelled indexes are user
defined labels that can be of any datatype and can be used as an index.
Pandas Series can be created by loading the datasets from existing storage like SQL Database, CSV file, and Excel
file. Pandas Series can also be created from the lists, dictionary, and from any other scalar value.
Creating Series
Different ways of creating Series are:
• Creating an empty series
[1]: import pandas as pd
Emp=pd.Series()
• Creating a Series from a NumPy array
[1]: import pandas as pd
import numpy as np
data = np.array([10,30,50])
s1 = pd.Series(data)
print(s1)
0 10
1 30
2 50
dtype: int32
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