Overview of Plotly¶

# Essential imports for constructing data and plotting
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt 
import plotly.offline as py
import plotly.graph_objs as go 
import plotly.figure_factory as ff 

Plot with Matplotlib¶

# Create fake dataset to plot 
df = pd.DataFrame(np.random.rand(100, 4), columns=['A', 'B', 'C', 'D'])
df.plot()
plt.legend(loc='upper right')
plt.show()

../_images/P00-Plotly Basic Overview_3_0.png

Plot with Plotly¶

# Create a fake dataset to show in plot 
df = pd.DataFrame(np.random.rand(100, 4), columns=['A', 'B', 'C', 'D'])
data = ([
    {'x': df.index, 'y': df[col], 'name': col} for col in df.columns
])

# Create figure 
fig = go.Figure(data=data)
# Show figure 
py.iplot(fig)

Scatter Plot¶

np.random.seed(42)
random_x = np.random.randint(1, 101, 100)
random_y = np.random.randint(1, 101, 100)


data = [go.Scatter(
    x = random_x,
    y = random_y, 
    mode= 'markers'
)] 

fig = go.Figure(data=data)
fig.show() 

Layout¶

# Set random values for plotting 
np.random.seed(42)
random_x = np.random.randint(1, 101, 100)
random_y = np.random.randint(1, 101, 100)

# Create data and plot 
data = [go.Scatter(
    x = random_x,
    y = random_y, 
    mode = 'markers'
)]


# Create layout 
layout = go.Layout(
    title = 'Random Data Scatterplot', # Graph title
    xaxis = dict(title = 'Some random x-values'), # x-axis label
    yaxis = dict(title = 'Some random y-values'), # y-axis label
    hovermode ='closest' # handles multiple points landing on the same vertical
)

# Creare figure 
fig = go.Figure(data=data, layout=layout)

# Show figure 
fig.show() 

Customization¶

# Set random values for plotting 
np.random.seed(42)
random_x = np.random.randint(1, 101, 100)
random_y = np.random.randint(1, 101, 100)

# Create data and plot 
data = [go.Scatter(
    x = random_x,
    y = random_y, 
    mode = 'markers',
    # Change marker style 
    marker = dict(
        size = 12,
        color = 'rgb(51, 204, 153)',
        symbol = 'pentagon', 
        line = dict(
            width = 2, 
        )
    )
)]

# Create layout 
layout = go.Layout(
    title = 'Random Data Scatterplot', # Graph title
    xaxis = dict(title = 'Some random x-values'), # x-axis label
    yaxis = dict(title = 'Some random y-values'), # y-axis label
    hovermode ='closest' # handles multiple points landing on the same vertical
)

# Create figure
fig = go.Figure(data=data, layout=layout)

# Show figure 
fig.show()

Line Charts¶

np.random.seed(56)
x_values = np.linspace(0, 1, 100) # 100 evenly spaced values
y_values = np.random.randn(100) # 100 random values

# Create traces 
trace0 = go.Scatter(
    x = x_values, 
    y = y_values+5,
    mode= 'markers',
    name = 'markers'
)

trace1 = go.Scatter(
    x = x_values, 
    y = y_values,
    mode= 'lines+markers', 
    name = 'lines+markers'
)

trace2 = go.Scatter(
    x = x_values, 
    y = y_values-5, 
    mode= 'lines', 
    name = 'lines'
)

# Create data 
data = [trace0, trace1, trace2]

# Create layout 
layout = go.Layout(
    title = 'Line charts showing different methods.'
)

# Create 
fig = go.Figure(data=data, layout=layout)

# Show plot 
fig.show()

Reading Data and Plotting¶

# Read a csv file from local computer 
df = pd.read_csv("../data/population.csv", index_col=0)

# Examine first few rows
df.head()

	PopEstimate2010	PopEstimate2011	PopEstimate2012	PopEstimate2013	PopEstimate2014	PopEstimate2015	PopEstimate2016	PopEstimate2017
Name
Connecticut	3580171	3591927	3597705	3602470	3600188	3593862	3587685	3588184
Maine	1327568	1327968	1328101	1327975	1328903	1327787	1330232	1335907
Massachusetts	6564943	6612178	6659627	6711138	6757925	6794002	6823721	6859819
New Hampshire	1316700	1318345	1320923	1322622	1328684	1330134	1335015	1342795
Rhode Island	1053169	1052154	1052761	1052784	1054782	1055916	1057566	1059639

# Create traces 
traces = [go.Scatter(
    x = df.columns, 
    y = df.loc[name], 
    mode = 'markers+lines', 
    name = name 
)for name in df.index]

# Create layout 
layout = go.Layout(
    title = 'Population Estimates of the Six New England States.'
)

# Create figure 
fig = go.Figure(data=traces, layout=layout)

# Show figure 
fig.show()

Bar Charts¶

# Read data 
df = pd.read_csv("../data/2018WinterOlympics.csv")

# Examine first few rows 
df.head()

	Rank	NOC	Gold	Silver	Bronze	Total
0	1	Norway	14	14	11	39
1	2	Germany	14	10	7	31
2	3	Canada	11	8	10	29
3	4	United States	9	8	6	23
4	5	Netherlands	8	6	6	20

Create Simple Bar Plot¶

# Create traces 
data = [go.Bar(
    x = df['NOC'], # NOC stands for National Olympic Committee
    y = df['Total'], 
)]

# Create layout 
layout = go.Layout(
    title = '2018 Winter Olympic Medals by Country.'
)

# Create figure 
fig = go.Figure(data=data, layout=layout)

# Show figure 
fig.show()

# Take a look at data again..
df.head()

	Rank	NOC	Gold	Silver	Bronze	Total
0	1	Norway	14	14	11	39
1	2	Germany	14	10	7	31
2	3	Canada	11	8	10	29
3	4	United States	9	8	6	23
4	5	Netherlands	8	6	6	20

Comparison between Variables¶

# Create traces 
trace1 = go.Bar(
    x = df['NOC'], 
    y = df['Gold'], 
    name = 'Gold', 
    marker = dict(color='#FFD700')
)

trace2 = go.Bar(
    x = df['NOC'], 
    y = df['Silver'], 
    name = 'Silver', 
    marker = dict(color='#9EA0A1')
)

trace3 = go.Bar(
    x = df['NOC'], 
    y = df['Bronze'], 
    name = 'Bronze', 
    marker = dict(color='#CD5F32')
)

# Store traces in data 
data = [trace1, trace2, trace3]

# Create layout 
layout = go.Layout(
    title = '2018 Winter Olympic Medals by Country.'
)

# Create figure 
fig = go.Figure(data=data, layout=layout)

# Show figure 
fig.show()

Stacked Bar Plot¶

# Create traces 
trace1 = go.Bar(
    x = df['NOC'], 
    y = df['Gold'], 
    name = 'Gold', 
    marker = dict(color='#FFD700')
)

trace2 = go.Bar(
    x = df['NOC'], 
    y = df['Silver'], 
    name = 'Silver', 
    marker = dict(color='#9EA0A1')
)

trace3 = go.Bar(
    x = df['NOC'], 
    y = df['Bronze'], 
    name = 'Bronze', 
    marker = dict(color='#CD5F32')
)

# Store traces in data 
data = [trace1, trace2, trace3]

# Create layout 
layout = go.Layout(
    title = '2018 Winter Olympic Medals by Country.', 
    barmode = 'stack'
)

# Create figure 
fig = go.Figure(data=data, layout=layout)

# Show figure 
fig.show()

Bubble Plot¶

# Read data 
df = pd.read_csv("../data/mpg.csv")

# Examine first few rows 
df.head()

	mpg	cylinders	displacement	horsepower	weight	acceleration	model_year	origin	name
0	18.0	8	307.0	130	3504	12.0	70	1	chevrolet chevelle malibu
1	15.0	8	350.0	165	3693	11.5	70	1	buick skylark 320
2	18.0	8	318.0	150	3436	11.0	70	1	plymouth satellite
3	16.0	8	304.0	150	3433	12.0	70	1	amc rebel sst
4	17.0	8	302.0	140	3449	10.5	70	1	ford torino

data =  go.Scatter(
    x = df['horsepower'], 
    y = df['mpg'],
    text= df['name'], 
    mode = 'markers', 
    marker= dict(size=1.5 * df['cylinders']) # set the marker size 
)

layout = go.Layout(
    title = 'Veichke mpg vs. horsepower.', 
    xaxis = dict(title='horsepower'), 
    yaxis = dict(title='mpg'), 
    hovermode= 'closest'
)

# Create figure 
fig = go.Figure(data=data, layout=layout)

# Show plot 
fig.show()

Boxplots¶

# Load iris dataset
import seaborn as sns 
iris = sns.load_dataset('iris')

# Examine first few rows 
iris.head()

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa
3	4.6	3.1	1.5	0.2	setosa
4	5.0	3.6	1.4	0.2	setosa

Single Boxplot¶

data = go.Box(
    x = iris['species'], 
    y = iris['sepal_length'], 
    boxpoints= 'all', # display the original data points
    jitter=0.3, # spread them out so they all appear
    pointpos=-1.8    # offset them to the left of the box

)

# Create layout 

layout = go.Layout(
    title = 'Boxplot of Species and Sepal Length', 
    xaxis = dict(title='Species'), 
    yaxis = dict(title='Sepal Length')
)

# Create figure 
fig = go.Figure(data=data, layout=layout)

# Show figure 
fig.show()

Show Outliers¶

data = go.Box(
    x = iris['species'], 
    y = iris['petal_length'], 
    boxpoints= 'outliers'
)

# Create layout 

layout = go.Layout(
    title = 'Boxplot of Species and Petal Length', 
    xaxis = dict(title='Species'), 
    yaxis = dict(title='Petal Length'), 
)

# Create figure 
fig = go.Figure(data=data, layout=layout)

# Show figure 
fig.show()

Grouped Boxplot¶

# Trace-1 
trace1 = go.Box(
    x = iris['species'], 
    y = iris['petal_length'], 
    boxpoints= 'outliers', 
    name = 'Petal Length'
)

# Trace-2 
trace2 = go.Box(
    x = iris['species'], 
    y = iris['petal_width'], 
    boxpoints= 'outliers', 
    name = 'Petal Width'

)

# Trace-3 
trace3 = go.Box(
    x = iris['species'], 
    y = iris['sepal_length'], 
    boxpoints= 'outliers', 
    name = 'Sepal Length'

)

# Trace-4 
trace4 = go.Box(
    x = iris['species'], 
    y = iris['sepal_width'], 
    boxpoints= 'outliers', 
    name = 'Sepal Width'

)

# Create layout 
layout = go.Layout(
    title = 'Boxplot of Iris Dataset', 
)

# Create data 
data = [trace1, trace2, trace3, trace4]

# Create figure 
fig = go.Figure(data=data, layout=layout)

# Show figure 
fig.show()

Histograms¶

# Create data 
data = go.Histogram(
    x = iris['sepal_length'], 
)

# Create layout 
layout = go.Layout(
    title = 'Histogram of Sepal Length', 
    
)

# Create figure 
fig = go.Figure(data=data, layout=layout)

# Show figure 
fig.show()

df = pd.read_csv("../data/mpg.csv")
df.head()

	mpg	cylinders	displacement	horsepower	weight	acceleration	model_year	origin	name
0	18.0	8	307.0	130	3504	12.0	70	1	chevrolet chevelle malibu
1	15.0	8	350.0	165	3693	11.5	70	1	buick skylark 320
2	18.0	8	318.0	150	3436	11.0	70	1	plymouth satellite
3	16.0	8	304.0	150	3433	12.0	70	1	amc rebel sst
4	17.0	8	302.0	140	3449	10.5	70	1	ford torino

data = go.Histogram(
    x = df['mpg'], 
    xbins=dict(start=8, end=50, size=6)
)

layout = go.Layout(
    title = 'Histogram of MPG', 
)

fig = go.Figure(data=data, layout=layout)
fig.show()

data = go.Histogram(
    x = df['mpg'], 
    xbins=dict(start=8, end=50, size=1)
)

layout = go.Layout(
    title = 'Histogram of MPG', 
)

fig = go.Figure(data=data, layout=layout)
fig.show()

data = go.Histogram(
    x = df['mpg'], 
    xbins=dict(start=8, end=50, size=.5)
)

layout = go.Layout(
    title = 'Histogram of MPG', 
)

fig = go.Figure(data=data, layout=layout)
fig.show()

df = pd.read_csv("../data/arrhythmia.csv")
df.head()

	Age	Sex	Height
0	68	1	146
1	58	1	148
2	36	1	149
3	34	1	150
4	40	1	150

data = [go.Histogram(
    x=df[df['Sex']==0]['Height'],
    opacity=0.75,
    name = 'Male'
),
go.Histogram(
    x=df[df['Sex']==1]['Height'],
    opacity=0.75,
    name = 'Female'
)]


layout = go.Layout(
    title = 'Height Comparison by Gender',
    barmode='overlay'
)


fig = go.Figure(data=data, layout=layout)
fig.show()

x = np.random.randn(1000)
hist_data = [x]
group_labels = ['distplot']

fig = ff.create_distplot(hist_data, group_labels)
fig.show()

Heatmaps¶

df = pd.read_csv("../data/2010SantaBarbaraCA.csv")
df.head()

	LST_DATE	DAY	LST_TIME	T_HR_AVG
0	20100601	TUESDAY	0:00	12.7
1	20100601	TUESDAY	1:00	12.7
2	20100601	TUESDAY	2:00	12.3
3	20100601	TUESDAY	3:00	12.5
4	20100601	TUESDAY	4:00	12.7

# Create X, Y, Z data 
data = [go.Heatmap(
    x = df['DAY'], 
    y = df['LST_TIME'], 
    z = df['T_HR_AVG'].values.tolist(), 
    colorscale= 'Jet'
)]

# Create layout
layout = go.Layout(
    title = 'Hourly Temperatures, June 1-7, 2010', 
)

# Create figure
fig = go.Figure(data=data, layout=layout)
fig.show()

Data Visualization with Python

Overview of Plotly¶

Plot with Matplotlib¶

Plot with Plotly¶

Scatter Plot¶

Layout¶

Customization¶

Line Charts¶

Reading Data and Plotting¶

Bar Charts¶

Create Simple Bar Plot¶

Comparison between Variables¶

Stacked Bar Plot¶

Bubble Plot¶

Boxplots¶

Single Boxplot¶

Show Outliers¶

Grouped Boxplot¶

Histograms¶

Heatmaps¶