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Dec 6th (8:00 PM) 1985 Registered
Python - Data Operations
Neha Kumawat
2 years ago
Table of Contents
- Data Operations in Numpy
- Data Operations in Pandas
1. Pandas Series
2. Pandas DataFrame
3. Pandas Panel
Python handles data of various formats through two libraries, Pandas and Numpy. In this chapter, we will see some basic examples from each of the libraries on how to operate on data.
Data Operations in Numpy
An N-dimensional array type called ndarray is the most important object defined in NumPy. It describes the collection of items of the same type which can be accessed using a zero-based index. by different array creation routines, an instance of ndarray class can be constructed which is described later in the tutorial. The basic ndarray is created using an array function in NumPy as follows −
numpy.array
Following are some examples on Numpy Data handling.
Example 1
# more than one dimensions
import numpy as np
a = np.array([[1, 2], [3, 4]])
print(a)
The output is as follows −
[[1, 2]
[3, 4]]Example 2
# minimum dimensions
import numpy as np
a = np.array([1, 2, 3,4,5], ndmin = 2)
print(a)
The output is as follows −
[[1, 2, 3, 4, 5]]Example 3
# dtype parameter
import numpy as np
a = np.array([1, 2, 3], dtype = complex)
print(a)
The output is as follows −
[ 1.+0.j, 2.+0.j, 3.+0.j]Data Operations in Pandas
Pandas handle data through Series, Data Frame, and Panel. We will see some examples from each of these.
1. Pandas Series
Series is a one-dimensional labeled array, it is capable of holding data of any type (integer, string, float, python objects, etc.). The collection of axis labels is called index.
A pandas Series can be created using the following constructor −
pandas.Series( data, index, dtype, copy)
Example
Here we create a series from a Numpy Array.
#import the pandas library and aliasing as pd
import pandas as pd
import numpy as np
data = np.array(['a','b','c','d'])
s = pd.Series(data)
print(s)
Its output is as follows −
0 a
1 b
2 c
3 d
dtype: object
2. Pandas DataFrame
A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. A pandas DataFrame can be created using the following constructor −
pandas.DataFrame( data, index, columns, dtype, copy)
Let us now create an indexed DataFrame using arrays.
import pandas as pd
data = {'Name':['Tom', 'Jack', 'Steve', 'Ricky'],'Age':[28,34,29,42]}
df = pd.DataFrame(data, index=['rank1','rank2','rank3','rank4'])
print(df)
Its output is as follows −
Age Name
rank1 28 Tom
rank2 34 Jack
rank3 29 Steve
rank4 42 Ricky
3. Pandas Panel
A panel is a 3D container of data. The term Panel data is derived from econometrics and is partially responsible for the name pandas − pan(el)-da(ta)-s.
A Panel can be created using the following constructor −
pandas.Panel(data, items, major_axis, minor_axis, dtype, copy)
In the below example we create a panel from dict of DataFrame Objects
#creating an empty panel
import pandas as pd
import numpy as np
data = {'Item1' : pd.DataFrame(np.random.randn(4, 3)),
'Item2' : pd.DataFrame(np.random.randn(4, 2))}
p = pd.Panel(data)
print(p)
Its output is as follows −
Dimensions: 2 (items) x 4 (major_axis) x 5 (minor_axis)
Items axis: 0 to 1
Major_axis axis: 0 to 3
Minor_axis axis: 0 to 4
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