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

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…

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Coursera | Data Science | Python

Exploring Statistical Analysis in the Context of Correlation – Testing a Potential Moderator
Bydatapandasadmin January 24, 2016October 23, 2016

Exploring Statistical Analysis in the Context of Correlation Chosen Dataset I will be working with Data from the Gapminder dataset. This happens to be the same dataset I worked with under the Data Management and Visualization course assignments. As elaborated and discussed in under the Data Management and Visualization course assignments, I have chosen to focus on the country,…

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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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Business Intelligence - BI | Python

Superset Heroku Installation – Microsoft SQL Server Database Connection on Windows PC
Bydatapandasadmin January 6, 2018January 6, 2018

Clone this github repository into YOUR OWN repository and Edit the link in the Readme.md file from https://www.herokucdn.com/deploy/button.svg)](https://heroku.com/deploy?template=https://github.com/dugjason/superset-on-heroku to the link in YOUR OWN CLONED repository eg. https://www.herokucdn.com/deploy/button.svg)](https://heroku.com/deploy?template=https://github.com/benadaba/hrmr Now scroll down and COMMIT (save) the changes ALSO edit the requirements.txt file and include any database requirements you want installed . eg pymssql==2.1.3, pyodbc==4.0.21 (this version was generating an…

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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

Crab Recommender System – Framework in Python Example and installation Problem Fix
Bydatapandasadmin August 21, 2016November 14, 2016

This is just to show that the import errors which were encountered during the installation of Crab, a Recommender Framework in Python worked fine with the fixes I earlier outlined. These were the errors and how they were fixed: ImportError: No module named ‘scikits ImportError: No module named learn.base The code below is the…

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January 29, 2017 at 1:16 pm

This is my first time visit at here and i am genuinely
happy to read all at one place.

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Read_csv Is Not Returning A Dataframe Python Pandas

Aggregation – Pandas Numpy Python Series DataFrame

Exploring Statistical Analysis in the Context of Correlation – Testing a Potential Moderator

Matplotlib Pyplot Plt Python Pandas Data Visualization Plotting

Superset Heroku Installation – Microsoft SQL Server Database Connection on Windows PC

Rank Sort Series DataFrames in Python Pandas Numpy

Crab Recommender System – Framework in Python Example and installation Problem Fix

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