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Difference between revisions of "Fancy Plots using Plotly"

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m (Added Dataframe code)
 
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{{Nutshell|elegant plots for adding in to research papers.|title=}}
+
{{Nutshell|elegant plots for adding in to research papers.|title=Plotly}}
  
 
*This is a collection of simple plots using the plotly library.
 
*This is a collection of simple plots using the plotly library.
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==Line Plots==
 
==Line Plots==
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CSV Data:
+
CSV Data
  
 
<syntaxhighlight lang="python">
 
<syntaxhighlight lang="python">
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dog,9,8
 
dog,9,8
 
dog,10,6
 
dog,10,6
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</syntaxhighlight>Code:<syntaxhighlight lang="python">
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</syntaxhighlight>
 +
 
 +
Code
 +
 
 +
<syntaxhighlight lang="py" line="1">
 
import plotly.express as px
 
import plotly.express as px
 
import pandas as pd
 
import pandas as pd
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     else:
 
     else:
 
         fig.write_image(FIG_DIR + "/" + PLOT_NAME + "." + PLOT_TYPES[i], scale=5)
 
         fig.write_image(FIG_DIR + "/" + PLOT_NAME + "." + PLOT_TYPES[i], scale=5)
−
</syntaxhighlight><syntaxhighlight lang="python" line="1">
+
</syntaxhighlight>
 +
 
 +
Output
 +
 
 +
[[File:Cat vs dog cuteness.png|frameless|733x733px]]
 +
 
 +
==== Creating DataFrame from Arrays ====
 +
<syntaxhighlight lang="py">
 +
TOTAL_NUMBERS = 1000
 +
y = np.zeros(TOTAL_NUMBERS)
 +
x = np.zeros(TOTAL_NUMBERS)
 +
df = pd.DataFrame(data={"X_Label": x, "Y_Label": y})
 +
</syntaxhighlight>
 +
 
 +
==Scatter Plots==
 +
CSV Data
 +
 
 +
<syntaxhighlight lang="py" line="1">
 +
type,area,price
 +
Condo,900,100
 +
Apartment,565,250
 +
Condo,500,80
 +
Apartment,800,75
 +
Condo,750,100
 +
Condo,850,110
 +
Apartment,790,120
 +
Condo,755,60
 +
Apartment,325,125
 +
Condo,300,50
 +
</syntaxhighlight>
 +
 
 +
Code
 +
 
 +
<syntaxhighlight lang="py" line="1">
 
import plotly.express as px
 
import plotly.express as px
 
import pandas as pd
 
import pandas as pd
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PLOTS_DIR = "./plots"
 
PLOTS_DIR = "./plots"
−
PLOT_NAME = "cat_v_dog"
+
PLOT_NAME = "house_price"
 
PLOT_TYPES = ["svg", "png", "html", "pdf", "jpeg"]
 
PLOT_TYPES = ["svg", "png", "html", "pdf", "jpeg"]
 
FIG_DIR = PLOTS_DIR + "/" + PLOT_NAME
 
FIG_DIR = PLOTS_DIR + "/" + PLOT_NAME
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PLOT_HEIGHT = 300
 
PLOT_HEIGHT = 300
  
−
df = pd.read_csv('./data/sample.csv')
+
 
−
fig = px.line(df, x="age", y="cuteness", color="animal")
+
df = pd.read_csv('./data/scatter.csv')
 +
fig = px.scatter(df, x="area", y="price", color="type")
 +
 
 +
 
 
fig.update_layout(
 
fig.update_layout(
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     title="Cat vs Dog Cuteness",
+
     title="House Pricing",
−
     xaxis_title="Animal's Age",
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     xaxis_title="Area",
−
     yaxis_title="Cuteness Rating",
+
     yaxis_title="Price",
−
     legend_title="Animal",
+
     legend_title="House Price",
 
     font=dict(
 
     font=dict(
 
         family="Courier New, monospace",
 
         family="Courier New, monospace",
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         x=0.001)
 
         x=0.001)
 
)
 
)
−
 
 
fig.show()
 
fig.show()
 
# Save Plot
 
# Save Plot
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</syntaxhighlight>
 
</syntaxhighlight>
  
 +
Output
  
−
Output:
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[[File:House Pricing Plot.png|frameless|750x750px]]
  
−
[[File:Cat vs dog cuteness.png|frameless|733x733px]]
+
==Bar Plots==
 +
CSV Data
  
−
==Scatter Plots==
+
Code
−
<br />
 
  
−
==Bar Plots==
+
Output
−
<br />
 
  
 
==Radar Plots==
 
==Radar Plots==
−
<br />
+
CSV Data
 +
 
 +
Code
 +
 
 +
Output
 +
 
 +
==Pie Charts==
 +
CSV Data
 +
 
 +
Code
 +
 
 +
Output
  
 
==Bubble Charts==
 
==Bubble Charts==
−
<br />
+
CSV Data
 +
 
 +
Code
 +
 
 +
Output
  
 
==Box Plots==
 
==Box Plots==
−
<br />
+
CSV Data
 +
 
 +
Code
 +
 
 +
Output
  
 
==2D Histograms==
 
==2D Histograms==
−
<br />
+
CSV Data
 +
 
 +
Code
 +
 
 +
Output

Latest revision as of 13:47, 15 March 2022


  • This is a collection of simple plots using the plotly library.
  • It consists of elegant color schemes and easy to ready adjustable fonts.
  • The reason for using plotly is that it allows for HTML plots that can be scaled and zoomed after plotting.

Installation

We need the plotly-express and kaleido library.

Conda

conda install -c plotly plotly_express==0.4.0
conda install -c conda-forge python-kaleido

Pip

pip install plotly_express==0.4.0
pip install kaleido

Line Plots

CSV Data 

animal,age,cuteness
cat,1,5
cat,2,8
cat,3,12
cat,4,15
cat,5,14
cat,6,15
cat,7,16
cat,8,18
cat,9,17
cat,10,10
dog,1,12
dog,2,14
dog,3,18
dog,4,20
dog,5,19
dog,6,17
dog,7,14
dog,8,9
dog,9,8
dog,10,6

Code 

import plotly.express as px
import pandas as pd
from tqdm import tqdm

PLOTS_DIR = "./plots"
PLOT_NAME = "cat_v_dog"
PLOT_TYPES = ["svg", "png", "html", "pdf", "jpeg"]
FIG_DIR = PLOTS_DIR + "/" + PLOT_NAME
!mkdir -p $FIG_DIR

# Plot Size
PLOT_WIDTH = 800
PLOT_HEIGHT = 300


df = pd.read_csv('./data/sample.csv')
fig = px.line(df, x="age", y="cuteness", color="animal")
fig.update_layout(
    title="Cat vs Dog Cuteness",
    xaxis_title="Animal's Age",
    yaxis_title="Cuteness Rating",
    legend_title="Animal",
    font=dict(
        family="Courier New, monospace",
        size=14,
        color="RebeccaPurple"
    )
)

fig.update_layout(
    autosize=True,
    width=PLOT_WIDTH,
    height=PLOT_HEIGHT,
    margin=dict(
        l=50,
        r=50,
        b=50,
        t=50,
        pad=4
    ),
    legend=dict(
        yanchor="top",
        y=0.999,
        xanchor="left",
        x=0.001)
)

fig.show()
# Save Plot
for i in tqdm(range(len(PLOT_TYPES))):
    if PLOT_TYPES[i] == "html":
        fig.write_html(FIG_DIR + "/" + PLOT_NAME + "." + PLOT_TYPES[i])
    else:
        fig.write_image(FIG_DIR + "/" + PLOT_NAME + "." + PLOT_TYPES[i], scale=5)

Output

Cat vs dog cuteness.png

Creating DataFrame from Arrays

TOTAL_NUMBERS = 1000
y = np.zeros(TOTAL_NUMBERS)
x = np.zeros(TOTAL_NUMBERS)
df = pd.DataFrame(data={"X_Label": x, "Y_Label": y})

Scatter Plots

CSV Data 

type,area,price
Condo,900,100
Apartment,565,250
Condo,500,80
Apartment,800,75
Condo,750,100
Condo,850,110
Apartment,790,120
Condo,755,60
Apartment,325,125
Condo,300,50

Code 

import plotly.express as px
import pandas as pd
from tqdm import tqdm

PLOTS_DIR = "./plots"
PLOT_NAME = "house_price"
PLOT_TYPES = ["svg", "png", "html", "pdf", "jpeg"]
FIG_DIR = PLOTS_DIR + "/" + PLOT_NAME
!mkdir -p $FIG_DIR

# Plot Size
PLOT_WIDTH = 800
PLOT_HEIGHT = 300


df = pd.read_csv('./data/scatter.csv')
fig = px.scatter(df, x="area", y="price", color="type")


fig.update_layout(
    title="House Pricing",
    xaxis_title="Area",
    yaxis_title="Price",
    legend_title="House Price",
    font=dict(
        family="Courier New, monospace",
        size=14,
        color="RebeccaPurple"
    )
)

fig.update_layout(
    autosize=True,
    width=PLOT_WIDTH,
    height=PLOT_HEIGHT,
    margin=dict(
        l=50,
        r=50,
        b=50,
        t=50,
        pad=4
    ),
    legend=dict(
        yanchor="top",
        y=0.999,
        xanchor="left",
        x=0.001)
)
fig.show()
# Save Plot
for i in tqdm(range(len(PLOT_TYPES))):
    if PLOT_TYPES[i] == "html":
        fig.write_html(FIG_DIR + "/" + PLOT_NAME + "." + PLOT_TYPES[i])
    else:
        fig.write_image(FIG_DIR + "/" + PLOT_NAME + "." + PLOT_TYPES[i], scale=5)

Output

House Pricing Plot.png

Bar Plots

CSV Data

Code

Output

Radar Plots

CSV Data

Code

Output

Pie Charts

CSV Data

Code

Output

Bubble Charts

CSV Data

Code

Output

Box Plots

CSV Data

Code

Output

2D Histograms

CSV Data

Code

Output

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