Plotly - Advanced

Posted on Edited on Views:

For Example: Line Plots

📘 Download World Earthquake Data From 1906-2022

Line Plots with plotly.express

The plotly.express module (usually imported as px) contains functions that can create entire figures at once.
Plotly Express is a built-in part of the plotly library, and is the recommended starting point for creating most common figures.

1
2
3
4
5
6
7
8
9
10
11
12
13
import pandas as pd
import plotly.express as px

df = pd.read_csv('data.csv')

# Pre-processing
df['Year'] = pd.to_datetime(df['time']).dt.year
df["Country"] = df["place"].str.split(pat=',', expand=False).str.get(-1)
df["Max_mag"] = df.groupby(['Year'])['mag'].transform(max)

df = df.query("2012 <= Year <= 2022")
fig = px.line(df, x="Year", y="Max_mag", title='Max magnitude from 2000 to 2022')
fig.show()
  • Explanation

    • Pandas groupby splits all the records from your data set into different categories or groups and offers you flexibility to analyze the data by these groups.

      1
      2
      3
      4
      5
      6
      7
      8
      # Group by 'Year', and get the maximum of 'mag' column
      df.groupby(['Year'])['mag'].max()

      # Group by 'Year', get the maximum of 'mag' column, and fit the length of dataframe  
      df.groupby(['Year'])['mag'].transform(max)

      # Query '2022 >= Year >= 2021' and get the value of 'Max_mag' column
      df.query("2022 >= Year >= 2021")["Max_mag"]

Line Plots with Column Encoding Color

1
2
3
4
5
6
7
8
9
10
11
12
13
14
import pandas as pd
import plotly.express as px

df = pd.read_csv('data.csv')

# Pre-processing
df['Year'] = pd.to_datetime(df['time']).dt.year
df["Country"] = df["place"].str.split(pat=',', expand=False).str.get(-1)
df["Max_mag"] = df.groupby(['Year', 'magType'])['mag'].transform(max)

df = df.query("2012 <= Year <= 2022")
fig = px.line(df, x="Year", y="Max_mag", 
  title='Max magnitude from 2012 to 2022', color='magType')
fig.show()
  • Explanation

    • Pandas groupby splits all the records from your data set into different categories or groups and offers you flexibility to analyze the data by these groups.

      1
      2
      3
      4
      5
      6
      7
      8
      # Group by 'Year' and 'magType', and get the maximum of 'mag' column
      df.groupby(['Year', 'magType'])['mag'].max()

      # Group by 'Year' and 'magType', get the maximum of 'mag' column, and fit the length of dataframe  
      df.groupby(['Year', 'magType'])['mag'].transform(max)

      # Query '2022 >= Year >= 2021' and get the value of 'Max_mag' column
      df.query("2022 >= Year >= 2021")["Max_mag"]

Basic Settings

Line charts with Markers

  • Set Title

    • title = 'Max magnitude and Min depth from 2012 to 2022'

  • Add one more field on text label

    • text = "Min_depth"

      1
      2
      3
      4
      5
      6
      7
      8
      9
      10
      11
      12
      13
      14
      15
      16
      import pandas as pd
      import plotly.express as px

      df = pd.read_csv('data.csv')

      # Pre-processing
      df['Year'] = pd.to_datetime(df['time']).dt.year
      df["Country"] = df["place"].str.split(pat=',', expand=False).str.get(-1)
      df["Max_mag"] = df.groupby(['Year', 'magType'])['mag'].transform(max)
      df["Min_depth"] = df.groupby(['Year', 'magType'])['depth'].transform(min)

      query_df = df.query("2022 >= Year >= 2012 & magType in ['mw', 'ml', 'ms', 'mb']")
      fig = px.line(query_df, x="Year", y="Max_mag", 
        title='Max magnitude and Min depth from 2012 to 2022', 
        color='magType', text="Min_depth")
      fig.show()

  • Mark data points

    • Method 1: markers = True

    • Method 2: symbol = "magType"

      1
      2
      3
      4
      5
      6
      7
      8
      9
      10
      11
      12
      13
      14
      15
      16
      17
      18
      19
      20
      21
      22
      23
      import pandas as pd
      import plotly.express as px

      df = pd.read_csv('data.csv')

      # Pre-processing
      df['Year'] = pd.to_datetime(df['time']).dt.year
      df["Country"] = df["place"].str.split(pat=',', expand=False).str.get(-1)
      df["Max_mag"] = df.groupby(['Year', 'magType'])['mag'].transform(max)

      query_df = df.query("2022 >= Year >= 2012")

      # Method 1: markers
      fig = px.line(query_df, x="Year", y="Max_mag", 
        title='Max magnitude from 2012 to 2022', 
        color='magType', markers=True)
      fig.show()

      # Method 2: symbol
      fig = px.line(query_df, x="Year", y="Max_mag", 
        title='Max magnitude from 2012 to 2022', 
        color='magType', symbol="magType")
      fig.show()

Save a Plot

1
2
# Install Package
$ pip install -U kaleido

  • Save a Figure in xxx Format: PNG, JPEG, SVG, PDF

    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    # PNG
    fig.write_image("fig1.png")

    # JPEG
    fig.write_image("fig1.jpeg")

    # SVG
    fig.write_image("fig1.svg")

    # PDF
    fig.write_image("fig1.pdf")

  • Change Image dimension and Scale

    1
    fig.write_image('fig2_scale2.jpeg', width=600, height=350, scale=2)

  • Interactive HTML export in Plotly Python

    1
    fig.write_html("fig1.html", auto_open=True)

Advanced: Interactive Custom Controls

Basic Range Slider and Range Selectors

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
import plotly.graph_objects as go
import pandas as pd

# Load data
df = pd.read_csv('data.csv')

# Pre-processing
df['Year'] = pd.to_datetime(df['time']).dt.year
df["Max_mag"] = df.groupby(['Year', 'magType'])['mag'].transform(max)

# Query
query_df = df.query("magType == 'mww'")

# Create figure
fig = go.Figure()

fig.add_trace(
    go.Scatter(x=list(query_df.time), y=list(query_df.Max_mag)))

# Set title
fig.update_layout(
    title_text="Time series with range slider and selectors"
)

# Add range slider
fig.update_layout(
    xaxis=dict(
        rangeselector=dict(
            buttons=list([
                dict(count=1, label="1m", step="month", stepmode="backward"),
                dict(count=6, label="6m", step="month", stepmode="backward"),
                dict(count=1, label="YTD", step="year", stepmode="todate"),
                dict(count=1, label="1y", step="year", stepmode="backward"),
                dict(step="all")
            ])
        ),
        rangeslider=dict( visible=True ),
        type="date"
    )
)

fig.show()

References