Python Basic - Pandas, Numpy -
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Introduction of Numpy NumPy is a Python package. It stands for 'Numerical Python', and Numpy is a linear algebra library to work with dimensional arrays, which contains useful linear algebra routines and random number capabilities.
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Numpy arrange() method The arange() method in the Numpy module in Python is used to generate linear sequence of numbers. If does it on the basis of the pre-provide starting and ending points along with a constant step size.
Syntax
import numpy as np
start = 1 # default 0
stop = 21
step = 1 # default 1
none = int
np.arange(start, stop, step, dtype=none)
Output
Omit
data = np.arange(start, stop, step)
data
Output
Combination of reshape() method
data = np.arange(start, stop, step).reshape(4,5)
data
Output
Combination of array() method
title = np.array(['UserId', 'SomethingId', 'ProductName', 'Price', 'Ratings'])
df = pd.DataFrame(data, columns=title)
df
Output
Pick up (Slice) specific data
df_part = pd.DataFrame(data[:, 3:], columns=title[3:])
df_part
Output
df_part = pd.DataFrame(data[:, :3], columns=title[:3])
df_part
Output
- Pandas Pandas is a library providing fast, flexible, and expressive way to work with a relational or table of data, both easily and intuitive. It allows you to process your data in a way similar to SQL. Scikit-learn is a library of classic machine learning algorithms. It features various classification, regression, and clustering algorithms, including support virtual machines, random force, and a lot more.
concat() method
df12 = pd.concat([df_part2, df_part1], axis=1)
df12
Output
References: LINKS
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