Python

Read_csv Is Not Returning A Dataframe Python Pandas

Bydatapandasadmin August 7, 2016

I was working with a CSV file on a project and after reading the CSV into the Program with the pandas.read_csv function the returned data type seemed not be a Dataframe for some reason

code was as follows:

sample = pd.read_csv(‘sample.csv’)

When i tried to run some functions on the supposed ‘returned dataframe object -sample’ I get an error which states- TypeError: ‘str’ object is not callable

Fix: It appeared to be an update issue so , I ran an update on my Anaconda package using conda update anaconda and that fixed the issue.

Hope it helps someone!

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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

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…

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Coursera | Data Science | Python

Running a Random Forest – Data Analysis and Intrepretation
Bydatapandasadmin March 25, 2016October 23, 2016

Overview My research work deals with Ghana, a country from the Gapminder dataset as has already been discussed from the beginning and progression through this course. The variables in my observation dataset are all quantitative. For the purposes of this assignment, I have binned my quantitative target variable, Life Expectancy (lifeexpectancy) into a 2-level binary categorical target variable. I have named…

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Python

Matplotlib Pyplot Plt Python Pandas Data Visualization Plotting

Bydatapandasadmin August 26, 2016October 23, 2016

This is some quick notes about graphing or plotting graphs with Matplotlib in Python. Credits to sentdex. You can check him out on Youtube.

import matplotlib.pyplot as plt

%matplotlib inline

# simple syntax is matplotlib plots as plt.plot(x, y)

plt.plot([1,2,3], [5,6,7])

plt.show()

In [4]:

# plotting with variables and basic labelling of our plot

x = [1,2,3]

y = [5,6,7]

##adding a legend to the plot

#add another line vectors first

x2 = [4,6,8]

y2= [10,14,18]

plt.plot(x, y, label='First Line') # you have to give the plots a label before you can call the legends on them

plt.plot(x2, y2, label='Second Line')

plt.xlabel('Plot number')

plt.ylabel('Important var')

plt.title('Plotting\nNice Graph') #\nGives you a sub title

plt.legend() #call the legend

plt.show()

In [10]:

#plotting barcharts and histograms

x3 = [2,4,6,8,10]

y3 = [3,5,7,9,11]

#second set of bars

x4 = [1,3,5,7,9]

y4 = [2,6,6,12,4]

#plot barchart

plt.bar(x3, y3, label='Bar1', color='r')

plt.bar(x4,y4, label='Bar2', color ='c')

plt.xlabel('x3')

plt.ylabel('y3')

plt.title('Bar Chart')

plt.legend()

plt.show()

In [14]:

#plotting histogram.

population_dist = [24,45,5,6,42,65,34,75,75,34,75,7,24,26,87,98,98,67,86, 65,63, 87,98,76,53,35]

#ids = [x for x in range(len(population_dist))] for bar chart plotting

#plot barchart

#plt.bar(ids, population_dist, label='hist')

#histogram works with bins so we will have to create bin 'containers' for our distribution

bins = [10,20,30,40,50,60,70,80,90,100]

plt.hist(population_dist, bins, histtype='bar', rwidth=0.5)

plt.xlabel('x3')

plt.ylabel('y3')

plt.title('Histogram')

plt.legend()

plt.show()

In [20]:

## scatter plot.

#basic skeletion

x = [1,2,3,4,5,6,7,8,9]

y = [4,5,8,3,6,9,5,4,8]

plt.scatter(x,y, label='ScatterShow', color='c', marker='^' , s=60) # s = size of the marker

plt.xlabel('x3')

plt.ylabel('y3')

plt.title('Scatter')

plt.legend()

plt.show()

In [23]:

#stacked plot

days = [1, 2, 3, 4, 5]

sleeping = [6,8,7,3,8]

eating = [6,9,7,3,6]

working = [9,8,5,7,8]

#working around legends for stackplots

plt.plot([],[], color='red', label='eating', linewidth=2)

plt.plot([],[], color='blue', label='sleeping', linewidth=2)

plt.plot([],[], color='yellow', label='working', linewidth=2)

plt.stackplot(days, eating, sleeping, working, color=['red','blue','yellow'])

plt.xlabel('x3')

plt.ylabel('y3')

plt.title('Stacked Plot')

plt.legend()

plt.show()

In [30]:

#pie charts

days = [1, 2, 3, 4, 5]

sleeping = [6,8,7,3,8]

eating = [6,9,7,3,6]

working = [9,8,5,7,8]

slices = [2,4,6,8,1]

colors = ['red','blue','green','brown','pink']

activities = ['sleeping','eating','working','dancing','travelling']

plt.pie(slices, labels=activities, colors=colors, startangle=90, shadow=True, explode=(0,0.1,0,0,0), autopct='%1.1f%%')

Out[30]:

([<matplotlib.patches.Wedge at 0x814e588>,

<matplotlib.patches.Wedge at 0x7fb7080>,

<matplotlib.patches.Wedge at 0x96f9ba8>,

<matplotlib.patches.Wedge at 0x82853c8>,

<matplotlib.patches.Wedge at 0x785a080>],

[<matplotlib.text.Text at 0x82ac4a8>,

<matplotlib.text.Text at 0x96f9438>,

<matplotlib.text.Text at 0x8026390>,

<matplotlib.text.Text at 0x94ac400>,

<matplotlib.text.Text at 0x785ada0>],

[<matplotlib.text.Text at 0x7fb7fd0>,

<matplotlib.text.Text at 0x96f97b8>,

<matplotlib.text.Text at 0x8285f98>,

<matplotlib.text.Text at 0x94aca58>,

<matplotlib.text.Text at 0x65bf860>])

In [48]:

#modifying the matplotlib

#plotting barchart.

x3 = [2,4,6,8,10]

y3 = [3,5,7,9,11]

#second set of bars

x4 = [1,3,5,7,9]

y4 = [2,6,6,12,4]

fig = plt.figure(facecolor='#f0f0f0') #set facecolour or background colour which will be maintained once we save our graph

ax1 = plt.subplot2grid((1,1),(0,0))

#tilt the xlabels

for labels in ax1.xaxis.get_ticklabels():

labels.set_rotation(45)

#plot barchart

ax1.bar(x3, y3, label='Bar1', color='r')

ax1.bar(x4,y4, label='Bar2', color ='c')

ax1.grid(True) #add a grid

plt.xlabel('x3')

plt.ylabel('y3')

plt.title('Bar Chart')

plt.legend()

#customise the legend

leg = ax1.legend(loc=2,ncol=6, prop={'size':10})

leg.get_frame().set_alpha(0.4)

##define spacing around the ticks and labels

plt.subplots_adjust(left = 0.09, bottom=0.20, right=0.94, top= 0.93, wspace = 0.2, hspace = 0)

plt.show()

#lets save the graph

fig.savefig('matplotlib.png', facecolor=fig.get_facecolor())

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Python

Python Pandas Pivot Table Index location Percentage calculation on Two columns

Bydatapandasadmin October 5, 2016October 23, 2016

pivot table for year on year This is a quick example of how to use pivot_table, to calculate year on year percentage sales . In [162]:

import sqlalchemy

import pyodbc

import numpy as np

from pandas import DataFrame

from bokeh.plotting import figure, output_file, show

import pandas as pd

In [163]:

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 [164]:

1 2	#lets check the head df.head()

Out[164]: First Name Last Name Sex Phone Fax Email Address Booking Date Departure Date Arrival Date Address1 … Number of Passengers…

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]:…

One Comment

cochinwebhosting says:

January 29, 2017 at 1:16 pm

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Read_csv Is Not Returning A Dataframe Python Pandas

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

Opening and Reading Text Files by Buffer Size – Python

Running a Random Forest – Data Analysis and Intrepretation

Matplotlib Pyplot Plt Python Pandas Data Visualization Plotting

Python Pandas Pivot Table Index location Percentage calculation on Two columns

Quick Example and Walk-Through JSON with Python

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