Installing Necessary Libraries¶

In [1]:

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

%matplotlib inline

In [2]:

from plotly.offline import iplot
import plotly as py
import plotly.tools as tls
import chart_studio.plotly as cpy
import chart_studio.tools as ctls

In [3]:

import cufflinks as cf

Using `cufflinks` and `iplot()` to create different plots¶

line
scatter
bar
box
spread
histogram
bubble
surface
heatmap

Knowing the version of the packages¶

In [1]:

print(py.__version__)

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-2221253c3501> in <module>
----> 1 print(py.__version__)

NameError: name 'py' is not defined

In [10]:

print(cf.__version__)

0.17.3

Embedding Online Plots to the Jupyter Notebook¶

In [4]:

ctls.embed('https://plotly.com/~cufflinks/8')

Out[4]:

Using Plotly-Cufflinks offline¶

In [4]:

py.offline.init_notebook_mode(connected=True)

In [5]:

cf.go_offline()

Connecting Jupyter Notebook with Plotly Account¶

In [11]:

import chart_studio
chart_studio.tools.set_credentials_file(username='PriyankaJha', api_key='EgKBYXoVIi8Aw32QlKrf')

`lineplot`¶

In [6]:

#generating some pseudo data for analysis
df = pd.DataFrame(np.random.randn(100, 3), columns=['A', 'B', 'C'])
df['A'] = df['A'].cumsum()
df['B'] = df['B'].cumsum()
df['C'] = df['C'].cumsum()
df.head()

Out[6]:

	A	B	C
0	-0.405230	0.070950	-2.137773
1	0.111522	1.920144	-2.159755
2	-0.626906	3.780232	-2.015929
3	0.716704	3.244089	-1.326465
4	0.211570	2.747247	-1.485680

In [7]:

#different themes for plots in plotly
cf.getThemes()

Out[7]:

['ggplot', 'pearl', 'solar', 'space', 'white', 'polar', 'henanigans']

In [12]:

df.iplot(theme='solar')

`scatterplot`¶

In [13]:

df.iplot(x='A', y='B', mode='markers', symbol='diamond', size=9, theme='solar')
#use box or lasso select to highlight the desired markers

`barplot`¶

In [10]:

titanic = sns.load_dataset('titanic')
titanic.head()

Out[10]:

	survived	pclass	sex	age	sibsp	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

In [12]:

titanic.iplot(kind='bar',
              x='sex',
              y='survived',
              theme='solar',
              color='lightgreen',
              xTitle='Sex',
              yTitle='Number of Survived Individuals',
              title='Survival on the Basis of Sex')

`stackedplot`¶

In [14]:

df.iplot(kind='bar', bargap=0.4, barmode='stack', theme='solar')

In [14]:

#horizontal bar graph
df.iplot(kind='barh', bargap=0.4, theme='solar')

`boxplot`¶

In [15]:

df.iplot(kind='box', theme='solar')

`areaplot`¶

In [16]:

#generating another set of dataset with only positive values
df2 = pd.DataFrame(np.random.randn(150, 3), columns=['A', 'B', 'C'])
df2['A'] = df2['A'].cumsum() + 15
df2['B'] = df2['B'].cumsum() + 15
df2['C'] = df2['C'].cumsum() + 15
df2.head()

Out[16]:

	A	B	C
0	15.161858	15.444999	15.744817
1	16.349772	15.269878	16.264184
2	15.119788	13.444587	15.544242
3	15.642109	11.379189	14.308738
4	16.190989	12.725358	13.952934

In [18]:

df2.iplot(kind='area', fill=True, theme='solar')

`surfaceplot`¶

In [19]:

df3 = pd.DataFrame({
    'X': [10, 20, 30, 20, 10],
    'Y': [10, 20, 30, 20, 10],
    'Z': [10, 20, 30, 20, 10]
})
df3

Out[19]:

	X	Y	Z
0	10	10	10
1	20	20	20
2	30	30	30
3	20	20	20
4	10	10	10

In [19]:

#color scales for surface and heatmaps
cf.colors.scales()

accent

blues

brbg

bugn

bupu

dark2

dflt

ggplot

gnbu

greens

greys

henanigans

oranges

original

orrd

paired

pastel1

pastel2

piyg

plotly

polar

prgn

pubu

pubugn

puor

purd

purples

rdbu

rdgy

rdpu

rdylbu

rdylgn

reds

set1

set2

set3

spectral

ylgn

ylgnbu

ylorbr

ylorrd

In [20]:

df3.iplot(kind='surface', colorscale='spectral')

In [140]:

# help(cf.datagen)

In [21]:

cf.datagen.sinwave(12, 0.15).iplot(kind='surface', colorscale='set1')

`3Dsacatterplot`¶

In [23]:

cf.datagen.scatter3d(2, 150, mode='stocks').iplot(kind='scatter3d',
                                                  x='x',
                                                  y='y',
                                                  z='z')

`spreadplot`¶

In [24]:

df[['A', 'B']].iplot(kind='spread', theme='solar')

C:\Users\Kripanand\anaconda3\lib\site-packages\cufflinks\plotlytools.py:849: FutureWarning:

The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead

C:\Users\Kripanand\anaconda3\lib\site-packages\cufflinks\plotlytools.py:850: FutureWarning:

The pandas.np module is deprecated and will be removed from pandas in a future version. Import numpy directly instead

`histogram`¶

In [25]:

df.iplot(kind='hist', bins=25, barmode='stack', theme='solar')

`bubbleplot`¶

In [26]:

cf.datagen.bubble3d(5, 6, mode='stocks').iplot(kind='bubble3d',
                                               x='x',
                                               y='y',
                                               z='z',
                                               size='size')

`heatmap`¶

In [27]:

cf.datagen.heatmap(17, 8).iplot(kind='heatmap',
                                colorscale='brbg',
                                title='Heatmap',
                                theme='solar')

Installing Necessary Libraries¶

Using cufflinks and iplot() to create different plots¶

Knowing the version of the packages¶

Embedding Online Plots to the Jupyter Notebook¶

Using Plotly-Cufflinks offline¶

Connecting Jupyter Notebook with Plotly Account¶

lineplot¶

scatterplot¶

barplot¶

stackedplot¶

boxplot¶

areaplot¶

surfaceplot¶

3Dsacatterplot¶

spreadplot¶

histogram¶

bubbleplot¶

heatmap¶