Python

ImportError: No module named ‘scikits’ – Python Fix Jupyter Notebook

Bydatapandasadmin August 21, 2016November 14, 2016

If you have the above problem, to fix this, follow these guidelines as advised by Flowerowl at this link (https://github.com/muricoca/crab/issues/92):

The steps are as follows:

Don’t install crab in this way: pip install crab / easy_install crab
You can find crab in pypi.python.org, this is not our scikits.crab source
Solution:
git clone https://github.com/muricoca/crab.git
python setup.py install
good luck ~

Now the details:

Visit the github link and clone or download. In this guide, I will download
Download and unzip into your default windows working directory
Now go to command prompt on windows and change into the crab-master directory and further into it, to get to the install file
Now run the python setup.py install command
Scikits.crab will be installed. Just note the path to the installation package
Now add this path to your system variables to make it work.
These are quick steps to access your system variables on Windows 10 PC
Type “variables” or “system variables” into your start menu “search box”

9. From the resulting window, click on the Advanced Tab and then Environment Variables

10. Locate the “Path” and click on “Edit…”

11. Click on “New” and then Add your path

12. To test it, now, change into your Python environment by typing “python” at the command prompt

13. And then import try and import from scikits.crab import datasets

13. It works fine in Jupyter notebook.

14. Hope this helps you 🙂

15. To illustrate fixes worked fine and code runs fine, check the crab example code here:Crab Recommender System installation error fix example code

16. Watch a quick video of the fix here:

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Data Science | Excel | Python | R - Programming | Resources | SQL

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…

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Python

Python Bokeh plotting Data Exploration Visualization And Pivot Tables Analysis

Bydatapandasadmin October 4, 2016October 23, 2016

This is a quick walk through Bokeh data exploration and visualization and also python pivot_tables (credit to pbpython on the pivot_tables).Visualization Dashboard In [49]:

import sqlalchemy

import pyodbc

from pandas import DataFrame

from bokeh.plotting import figure, output_file, show

import pandas as pd

In [50]:

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 top 10 * 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 [51]:

1 2	#lets look at the head of the dataframe df.head()

Out[51]: First Name Last Name Sex Phone Fax Email Address Booking Date Departure Date Arrival Date Address1 … Number of Passengers Total Cost Currency…

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

Aggregation – Pandas Numpy Python Series DataFrame
Bydatapandasadmin August 17, 2016October 23, 2016

In this quick notes, we will go through aggregation in Python. This is part of lectures on Learning Python for Data Analysis and Visualization by Jose Portilla on Udemy.

Default

1
2
3

import numpy as np
import pandas as pd
from pandas import Series, DataFrame

url = “http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/” In [2]:

Default

1

dframe_wine = pd.read_csv('winequality-red.csv', sep=';')

In [4]:

Default

1

dframe_wine.head()

Out[4]: fixed acidity volatile acidity citric acid residual sugar chlorides free sulfur dioxide total sulfur dioxide density…

Read More Aggregation – Pandas Numpy Python Series DataFrame
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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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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ImportError: No module named ‘scikits’ – Python Fix Jupyter Notebook

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

Python Bokeh plotting Data Exploration Visualization And Pivot Tables Analysis

Natural Language Processing Machine Learning Algorithm Model Python – NLP

Aggregation – Pandas Numpy Python Series DataFrame

Opening and Reading Text Files by Buffer Size – Python

Matplotlib Pyplot Plt Python Pandas Data Visualization Plotting

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