Python

Opening and Reading Text Files by Buffer Size – Python

Bydatapandasadmin March 21, 2016August 15, 2016

Today, I went through “Opening and Reading Text Files by Buffer Size” with a friend of mine who wanted to grasp the basic concepts of Python

This is a simple but straightforward tutorial into the “Opening and Reading Text Files by Buffer Size

We further delved into opening and reading binary files. In our case was opening and reading images and renaming them.

Sample code from idle shell

opening and reading txt files1

The textbook for this work was Python: Learn Python in One Day and Learn It Well by Jamie Chan

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Data Science | Machine Learning | Python

Natural Language Processing Machine Learning Algorithm Model Python – NLP
Bydatapandasadmin September 4, 2016August 12, 2017

In this guide, we take a look at Natural Language Processing, NLP in Python. Natural Language is a very extensive topic and very powerful machine learning algorithm you can build. This is very useful in many areas of most industries. Credits to Jose Portilla, creator of Learning Python for Data Analysis and Visualization course on Udemy …

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Python

Array Processing – Python Numpy – How to work with Arrays in Python

Bydatapandasadmin August 19, 2016October 23, 2016

Hi Guys, Thanks for all your emails. In this note, we will be looking at Array Processing in Python. This is part of lectures on Learning Python for Data Analysis and Visualization by Jose Portilla on Udemy. In [3]:

import numpy as np

import matplotlib.pyplot as plt

%matplotlib inline

In [4]:

1	points = np.arange(-5,5,0.01)

In [5]:

1	dx, dy = np.meshgrid(points,points)

In [6]:

Out[6]:

array([[-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99],

[-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99],

...,

[-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99],

[-5. , -4.99, -4.98, ..., 4.97, 4.98, 4.99]])

In [7]:

Out[7]:

array([[-5. , -5. , -5. , ..., -5. , -5. , -5. ],

[-4.99, -4.99, -4.99, ..., -4.99, -4.99, -4.99],

[-4.98, -4.98, -4.98, ..., -4.98, -4.98, -4.98],

...,

[ 4.97, 4.97, 4.97, ..., 4.97, 4.97, 4.97],

[ 4.98, 4.98, 4.98, ..., 4.98, 4.98, 4.98],

[ 4.99, 4.99, 4.99, ..., 4.99, 4.99, 4.99]])

In [8]:…

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 | Python

Python iloc, loc, ix Data Retrieving Selection Functions
Bydatapandasadmin September 5, 2016October 23, 2016

Pandas iloc, loc, and ix functions are very powerful ways to quickly select data from your dataframe. Today , we take a quick look at these 3 functions. Credits to Data School, you can check him out in Youtube In [1]:

Default

1

import pandas as pd

In [2]:

Default

1
2

#lets get this public dataset and play with it
ufo = pd.read_csv('http://bit.ly/uforeports')

In [3]:

Default

1
2

#lets examine the head
ufo.head()

Out[3]: City Colors Reported Shape Reported State…

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Python

Getting Stock Prices from Yahoo and plotting Python 3 Matplolib Urllib

Bydatapandasadmin September 3, 2016October 23, 2016

This is some quick notes about getting stock data from Yahoo and plotting it using Matplotlib . The Python version used is Python 3.5 Credits to sentdex. You can check him out on Youtube. In [11]:

import matplotlib.pyplot as plt

import numpy as np

import urllib

import matplotlib.dates as mdate

import urllib.request

%matplotlib inline

#%matplotlib qt

#%matplotlib notebook

# interactive popout window plot

In [12]:

#getting data from the internet

#define our converter function

def bytespdate2num(fmt, encoding='utf-8'):

strconverter = mdate.strpdate2num(fmt)

def bytesconverter(b):

s = b.decode(encoding)

return strconverter(s)

return bytesconverter

In [13]:

#define a function which will take the stock data and plot the graph

def graph_data(stock):

stock_data_url = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'

source_code = urllib.request.urlopen(stock_data_url).read().decode()

stock_data =[]

split_source = source_code.split('\n') #split the code based on next line

#lets grab only the relevant stock data

for line in split_source:

split_line = line.split(',')

if(len(split_line))==6:

if 'values' not in line and 'labels' not in line:

stock_data.append(line)

date, closep, highp, lowp, openp, volume = np.loadtxt(stock_data, delimiter=',',unpack=True,

converters={0: bytespdate2num('%Y%m%d')})

plt.plot_date(date,closep,'-', label='Price')

plt.xlabel('Date')

plt.ylabel('Price')

plt.title('Interesting stock graph from yahoo')

plt.legend()

In [14]:

1 2	#lets plot TSLA stock prices. graph_data('TSLA')

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Data Science | Python

Quick Example and Walk-Through JSON with Python

Bydatapandasadmin November 13, 2016

This is a quick to create a dictionary object , write it to a JSON file and then read back the file and convert it to a dictionary and access the items in the dictionary. Credit to : codebasics. You can check him out on Youtube In [3]:

# lets create a dictionary of books

book = {}

book['Tom'] = {

'name':'Tom',

'address':'1 red street, NY',

'phone':98988988

}

book['Bob'] = {

'name':'Bob',

'address':'1 green street, NY',

'phone':252525885

}

In [6]:

#import json and pass the dictionary to it

import json

s = json.dumps(book)

Out[6]:

1	'{"Bob": {"phone": 252525885, "name": "Bob", "address": "1 green street, NY"}, "Tom": {"phone": 98988988, "name": "Tom", "address": "1 red street, NY"}}'

In [14]:…

Opening and Reading Text Files by Buffer Size – Python

Natural Language Processing Machine Learning Algorithm Model Python – NLP

Array Processing – Python Numpy – How to work with Arrays in Python

Save Python Pivot Table in Excel Sheets ExcelWriter

Python iloc, loc, ix Data Retrieving Selection Functions

Getting Stock Prices from Yahoo and plotting Python 3 Matplolib Urllib

Quick Example and Walk-Through JSON with Python

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