No categories assigned

Fancy Plots using Plotly

Revision as of 11:59, 18 February 2022 by Kaustubh (talk | contribs) (Line plots completed)


  • 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

Scatter Plots


Bar Plots


Radar Plots


Bubble Charts


Box Plots


2D Histograms