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Difference between revisions of "Fancy Plots using Plotly"
m (Scatter plots added) (Tag: Visual edit) |
m (Pie chart template added) (Tag: Visual edit) |
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==Line Plots== | ==Line Plots== | ||
| − | ==== CSV Data ==== | + | ====CSV Data==== |
<syntaxhighlight lang="python"> | <syntaxhighlight lang="python"> | ||
animal,age,cuteness | animal,age,cuteness | ||
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</syntaxhighlight> | </syntaxhighlight> | ||
| − | ==== Code ==== | + | ====Code==== |
<syntaxhighlight lang="py" line="1"> | <syntaxhighlight lang="py" line="1"> | ||
import plotly.express as px | import plotly.express as px | ||
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</syntaxhighlight> | </syntaxhighlight> | ||
| − | ==== Output ==== | + | ====Output==== |
[[File:Cat vs dog cuteness.png|frameless|733x733px]] | [[File:Cat vs dog cuteness.png|frameless|733x733px]] | ||
==Scatter Plots== | ==Scatter Plots== | ||
| − | ==== CSV Data ==== | + | ====CSV Data==== |
<syntaxhighlight lang="py" line="1"> | <syntaxhighlight lang="py" line="1"> | ||
type,area,price | type,area,price | ||
| Line 125: | Line 125: | ||
</syntaxhighlight> | </syntaxhighlight> | ||
| − | ==== Code ==== | + | ====Code==== |
<syntaxhighlight lang="py" line="1"> | <syntaxhighlight lang="py" line="1"> | ||
import plotly.express as px | import plotly.express as px | ||
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</syntaxhighlight> | </syntaxhighlight> | ||
| − | ==== Output ==== | + | ====Output==== |
[[File:House Pricing Plot.png|frameless|750x750px]] | [[File:House Pricing Plot.png|frameless|750x750px]] | ||
==Bar Plots== | ==Bar Plots== | ||
| − | ==== CSV Data ==== | + | ====CSV Data==== |
| − | ==== Code ==== | + | ====Code==== |
| − | ==== Output ==== | + | ====Output==== |
==Radar Plots== | ==Radar Plots== | ||
| − | ==== CSV Data ==== | + | ====CSV Data==== |
| − | ==== Code ==== | + | ====Code==== |
| − | ==== Output ==== | + | ====Output==== |
| + | |||
| + | ==Pie Charts== | ||
| + | |||
| + | ====CSV Data==== | ||
| + | |||
| + | ====Code==== | ||
| + | |||
| + | ====Output==== | ||
==Bubble Charts== | ==Bubble Charts== | ||
| − | ==== CSV Data ==== | + | ====CSV Data==== |
| − | ==== Code ==== | + | ====Code==== |
| − | ==== Output ==== | + | ====Output==== |
==Box Plots== | ==Box Plots== | ||
| − | ==== CSV Data ==== | + | ====CSV Data==== |
| − | ==== Code ==== | + | ====Code==== |
| − | ==== Output ==== | + | ====Output==== |
==2D Histograms== | ==2D Histograms== | ||
| − | ==== CSV Data ==== | + | ====CSV Data==== |
| − | ==== Code ==== | + | ====Code==== |
| − | ==== Output ==== | + | ====Output==== |
Revision as of 15:39, 18 February 2022
 | Plotly in a nutshell: elegant plots for adding in to research papers. |
- 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.
Contents
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
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
