Java Python R SQL Excel Compared Similarities For Data Science and Data Analytics – The Basics

Bydatapandasadmin June 9, 2017June 9, 2017

If you have ever worked with Java, Python, R, SQL, Excel and other Languages on a varied Data Science or Data Analytics projects, you will realise that all these languages have similar syntaxes, or at least, can achieve the same objective with very similar codes.

Below is a comparison and similarities of these various tools and programmes I put together for quick reference when I realised that there are some similarities between these languages languages and tools are.

Though it is not complete and exhaustive, I believe it will be of help to someone.

If you have any similarities amongst any of these languages, please leave it as a comment in the comment box I will update the table.

I would want this to help all new people who are into Data Science and Data Analytics as I wish I had something similar to this when I first started. Thank you for your contribution.

Navigating the table:

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Use the search box located directly on the top right hand corner of the table to search for terms
Use the scroll bar at the bottom of the table to see all columns
The table has 5 columns which are Java, Python, R, SQL, Excel, Example, Description

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Python | Uncategorized

Rank Sort Series DataFrames in Python Pandas Numpy

Bydatapandasadmin August 14, 2016October 23, 2016

In this quick notes, we will have a look at Rank and Sort in Series and DataFrames in Python In [1]:

import numpy as np

import pandas as pd

from pandas import Series, DataFrame

In [3]:

#make a series

ser1 = Series(range(3), index=['C','A','B'])

ser1

Out[3]:

C 0

A 1

B 2

dtype: int32

In [4]:

1 2	#sort index of series ser1.sort_index()

Out[4]:

A 1

B 2

C 0

dtype: int32

In [5]:

1 2	#order or sort by values ser1.order()

1 2	C:\Users\adaba\Anaconda3\lib\site-packages\ipykernel\__main__.py:2: FutureWarning: order is deprecated, use sort_values(...) from ipykernel import kernelapp as app

Out[5]:

C 0

A 1

B 2

dtype: int32

In [6]:

1	ser1.sort_values()

Out[6]:

C 0

A 1

B 2

dtype: int32

In [7]:

1	from numpy.random import randn

In [8]:

#make a series by passing in 10 random numbers

ser2 = Series(randn(10))

ser2

Out[8]:

0 -0.201751

1 1.534109

2 0.542825

3 1.421174

4 -0.173771

5 0.002573

6 -0.490208

7 -0.765340

8 -2.546040

9 -0.456699

dtype: float64

In [10]:

1 2	#sort the values ser2.sort_values()

…

Python

Array Transposition – Numpy Python Data Analysis

Bydatapandasadmin August 21, 2016October 23, 2016

Welcome Guys, We will be looking at Array transposition in this quick notes. This is part of lectures on Learning Python for Data Analysis and Visualization by Jose Portilla on Udemy. In [1]:

1	import numpy as np

In [2]:

1 2	arr = np.arange(50) arr

Out[2]:

array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,

17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,

34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])

In [17]:

1	arr.reshape((10,5))

Out[17]:

array([[ 0, 1, 2, 3, 4],

[ 5, 6, 7, 8, 9],

[10, 11, 12, 13, 14],

[15, 16, 17, 18, 19],

[20, 21, 22, 23, 24],

[25, 26, 27, 28, 29],

[30, 31, 32, 33, 34],

[35, 36, 37, 38, 39],

[40, 41, 42, 43, 44],

[45, 46, 47, 48, 49]])

In [10]:

arr.T

Out[10]:

array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,

17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,

34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])

In [11]:

1	np.dot(arr.T, arr)

Out[11]:

40425

In [23]:

1	arr3d = np.arange(50).reshape((5,5,12))

---------------------------------------------------------------------------

ValueError Traceback (most recent call last)

<ipython-input-23-8908d625ec66> in <module>()

----> 1 arr3d = np.arange(50).reshape((5,5,12))

ValueError: total size of new array must be unchanged

…

Resources

Basic Bash Commands For Quick Reference
ByBernard Adabankah July 22, 2018July 22, 2018

These are some common basic bash commands for your quick reference Credit: Big Data For Data Engineers – Coursera Command Description awk “Aho, Weinberger and Kernigan”, Bell Labs, 1970s. Interpreted programming language for text processing. awk -F (see above) + Set the field separator. cat Display the contents of a file at the command line, is…

Read More Basic Bash Commands For Quick Reference
Coursera | Data Science | Python

Coursera Capstone Project – Data Analysis and Interpretation
Bydatapandasadmin March 31, 2016April 25, 2016

What is it This week, I started the Data Analysis and Interpretation Capstone by Wesleyan University on Coursera. This is the final of 5 course specialisation. The capstone project is expected to take 4 weeks. With each week tackling and doing a major component of the Project work. What is the objective The objective of…

Read More Coursera Capstone Project – Data Analysis and Interpretation

Python | Uncategorized

Save Python Pivot Table in Excel Sheets ExcelWriter

Bydatapandasadmin October 5, 2016October 23, 2016

Untitled This is a quick script on how to save Python pivot_table in an excel file. Credit to pbpython In [1]:

import sqlalchemy

import pyodbc

from pandas import DataFrame

import pandas as pd

In [2]:

df = DataFrame

## connect to SqlServer Database and get information.

try:

import urllib

params = urllib.quote_plus("DRIVER={SQL Server Native Client 11.0};SERVER=.\MSSQLSERVER_ENT;DATABASE=Antrak;Trusted_Connection=yes;")

engine = sqlalchemy.create_engine('mssql+pyodbc:///?odbc_connect=%s"' % params)

resoverall = engine.execute("SELECT * FROM FlightBookingsMain")

df = df(resoverall.fetchall())

df.columns = resoverall.keys()

except (RuntimeError, TypeError, NameError):

print('Error in Conneccting')

print(RuntimeError, TypeError, NameError)

finally:

print("connected")

connected

In [8]:

df.head()

Out[8]: First Name Last Name Sex Phone Fax Email Address Booking Date Departure Date Arrival Date Address1 … Number of Passengers Total Cost Currency Numeric Code…

Data Science | Resources

DataSets Around the Web For Big Data, Machine Learning and DataScience Practices
ByBernard Adabankah September 8, 2018February 14, 2023

Here is a collection of several DataSet repositories, I have come across the web. ( I will adding more along the way) You can add to this by commenting on the post and I will update it in the this list Google Dataset Search : This is a great tool released by Google to help…

Read More DataSets Around the Web For Big Data, Machine Learning and DataScience Practices

Java Python R SQL Excel Compared Similarities For Data Science and Data Analytics – The Basics

Rank Sort Series DataFrames in Python Pandas Numpy

Array Transposition – Numpy Python Data Analysis

Basic Bash Commands For Quick Reference

Coursera Capstone Project – Data Analysis and Interpretation

Save Python Pivot Table in Excel Sheets ExcelWriter

DataSets Around the Web For Big Data, Machine Learning and DataScience Practices

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