Plotly for 循环中箱线图的子图 Python

Question

How can I make subplots by using a for loop when the traces for each individual subplot are already created using a for loop, what I got so far:

import plotly.express as px
import plotly.io as pio 
import kaleido 
pio.renderers.default='browser' #change to browser or svg
import plotly.graph_objects as go
from plotly.subplots import make_subplots

df1 = pd.DataFrame({'in1' : [100, 150, 110, 180, 125], 
                   'in2' : [200, 210, 125, 125, 293],
                   'in3' : [50, 35, 200, 100, 180]
                   })

a = ['c', 'd', 'e', 'f','g']
df1t = df1.T
df1t.columns= a

fig = make_subplots()

for col in df1t:
    fig.add_trace(go.Box(y=df1t[col].values, name=str(df1t[col].name)))
    
fig.show()

But I have multiple dataframes named df2, df3, df4, df5, df6, df7, df8 and df9 which are plotted in the same way as above with column name a. I want to make subplots of each df.

Answer 1

• have synthesised 9 additional data frames per your specification
• construct a dict of all defined variables df1 to df9. This is to enable simple looping
• now just a simple case of creating all the traces across all the data frames after creating the figure with appropriate number of rows

import plotly.express as px
import plotly.io as pio
import kaleido
import pandas as pd
import numpy as np

pio.renderers.default = "browser"  # change to browser or svg
import plotly.graph_objects as go
from plotly.subplots import make_subplots

df1 = pd.DataFrame(
    {
        "in1": [100, 150, 110, 180, 125],
        "in2": [200, 210, 125, 125, 293],
        "in3": [50, 35, 200, 100, 180],
    }
)

# fmt: off
df2 = pd.DataFrame((df1.values * np.random.uniform(0.8, 1.2, size=df1.shape)).astype(int),columns=df1.columns)
df3 = pd.DataFrame((df1.values * np.random.uniform(0.8, 1.2, size=df1.shape)).astype(int),columns=df1.columns)
df4 = pd.DataFrame((df1.values * np.random.uniform(0.8, 1.2, size=df1.shape)).astype(int),columns=df1.columns)
df5 = pd.DataFrame((df1.values * np.random.uniform(0.8, 1.2, size=df1.shape)).astype(int),columns=df1.columns)
df6 = pd.DataFrame((df1.values * np.random.uniform(0.8, 1.2, size=df1.shape)).astype(int),columns=df1.columns)
df7 = pd.DataFrame((df1.values * np.random.uniform(0.8, 1.2, size=df1.shape)).astype(int),columns=df1.columns)
df8 = pd.DataFrame((df1.values * np.random.uniform(0.8, 1.2, size=df1.shape)).astype(int),columns=df1.columns)
df9 = pd.DataFrame((df1.values * np.random.uniform(0.8, 1.2, size=df1.shape)).astype(int),columns=df1.columns)
# fmt: on

# create dictionary of dataframes, by inspecting defined variables
dfs = {
    varname: eval(varname)
    for varname in dir()
    if varname[0:2] == "df" and len(varname) == 3 and varname[2] in "123456789"
}

a = ["c", "d", "e", "f", "g"]

# create sub-plots based on number of dataframes
fig = make_subplots(rows=len(dfs.keys()))

# add traces to appropriate subplot for dataframe
for r, df in enumerate(dfs.values()):
    df1t = df.T
    df1t.columns = a

    for col in df1t:
        fig.add_trace(
            go.Box(y=df1t[col].values, name=str(df1t[col].name)), row=r + 1, col=1
        )

fig.show()