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!

Want more information like this?

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())

Want more information like this?

Java | Python

Python Similarities Comparison With Java – Short Code Snippets
Bydatapandasadmin March 3, 2017

Trying to learn new language when you already know another one is most at times easier to do. And it is much more so easy when the new language you want to pick up is very related to the one you already know in one way or the other. Both Java and Python are great…

Read More Python Similarities Comparison With Java – Short Code Snippets
Python

Basic Plotting Using Bokeh Python Pandas Library – Scatter, Line Visualizations
Bydatapandasadmin September 26, 2016October 23, 2016

Bokeh is a powerful framework for data visualization in Python. Here I take a look at straightforward plotting and visualization using this powerful library. Credit to PythonHow. You can check him out on Youtube bokeh.models #It is a low level interface which involves a lot of work bokeh.plotting # It is a middle level interface bokeh.chart #…

Read More Basic Plotting Using Bokeh Python Pandas Library – Scatter, Line Visualizations

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…

Coursera | Data Science | Python

Test a Logistic Regression Model – Data Analysis and Intrepretation
Bydatapandasadmin February 28, 2016October 23, 2016

OVERVIEW My research work deals with Ghana, a country from the Gapminder dataset. What I found in my logistic regression analysis. Discussion of the results for the associations between all of my explanatory variables and my response variable The primary quantitative explanatory variable in my regression analysis is the Income Per Person…

Read More Test a Logistic Regression Model – Data Analysis and Intrepretation
Azure Machine Learning | Data Science | Machine Learning | Python

Deploy Productionalise Azure Machine Learning Algorithm Using Python
ByBernard Adabankah July 24, 2018

This is a script to consume Azure Machine Learning Model which you build using Azure ML Studio. So basically this is a Request-Response Service (RRS) according to Microsoft. This was a Boosted Decision Tree Algorithm build with Census Adult Data which is available as one of the Sample DataSet in Azure ML. You can adapt the…

Read More Deploy Productionalise Azure Machine Learning Algorithm Using Python

One Comment

cochinwebhosting says:

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.

Reply

Read_csv Is Not Returning A Dataframe Python Pandas

Matplotlib Pyplot Plt Python Pandas Data Visualization Plotting

Python Similarities Comparison With Java – Short Code Snippets

Basic Plotting Using Bokeh Python Pandas Library – Scatter, Line Visualizations

Python Pandas Pivot Table Index location Percentage calculation on Two columns

Test a Logistic Regression Model – Data Analysis and Intrepretation

Deploy Productionalise Azure Machine Learning Algorithm Using Python

One Comment

Leave a Reply Cancel reply

DataPandas LTS

EXPLORE DataPandas

ImportAnt link

GET IN TOUCH

© 2026 DataPandas

Similar Posts

One Comment

Leave a Reply Cancel reply

DataPandas LTS

EXPLORE DataPandas

ImportAnt link

GET IN TOUCH

© 2026 DataPandas