Python method to read Excel drawing histogram

The following will introduce in detail how to use Pythonpyecharts、matplotlibandseabornThree libraries read data from Excel and draw histograms (Histogram). The histogram is used to show the data distribution, and the core is to count the frequency or frequency of different intervals. Each library's code example contains detailed descriptions of core classes, functions, and properties.

1. Use pyecharts to draw histograms

pyechartsIt is an interactive visualization library based on ECharts, but the frequency distribution of the histogram needs to be manually calculated (because there is no built-in histogram function).

Code example:

import pandas as pd
import numpy as np
from  import Bar
from pyecharts import options as opts
# Read Excel datadf = pd.read_excel("")
data = df["Numerical column"].tolist()  # Assume that the data column is named "numerical column"# Calculate the interval and frequency of the histogramcounts, bins = (data, bins=10)  # 10 intervalsx_labels = [f"{bins[i]:.1f}-{bins[i+1]:.1f}" for i in range(len(bins)-1)]
# Create a bar chart (simulate histogram)bar = Bar()
bar.add_xaxis(x_labels)
bar.add_yaxis("Frequency", ())
# Configure global optionsbar.set_global_opts(
    title_opts=(title="Numerical distribution histogram"),
    xaxis_opts=(name="Interval"),
    yaxis_opts=(name="Frequency"),
    toolbox_opts=()
)
# Render as HTML file("pyecharts_histogram.html")

Core classes and functions:

• Bar(): Bar chart object, used to simulate histograms.
• (): Calculate the frequency and interval of histogram (binsSpecify the number of intervals).
• add_xaxis()/add_yaxis(): Add X/Y axis data.
• set_global_opts(): Configure title, axis name, etc.

2. Use matplotlib to draw histograms

matplotlibIt is a basic drawing library with built-inhist()The function directly draws the histogram.

Code example:

import pandas as pd
import  as plt
# Read Excel datadf = pd.read_excel("")
data = df["Numerical column"]
# Create canvas and coordinate systemsfig, ax = (figsize=(10, 6))
# Draw histogram(
    data, 
    bins=10,              # Number of intervals    color="skyblue",      # color    edgecolor="black",    # Border color    alpha=0.7,            # Transparency    density=False         # False display frequency, True display frequency)
# Add title and tagax.set_title("Numerical distribution histogram", fontsize=14)
ax.set_xlabel("Numerical Range", fontsize=12)
ax.set_ylabel("Frequency", fontsize=12)
(axis="y", linestyle="--")  # Show horizontal grid lines# Show chartsplt.tight_layout()
("matplotlib_histogram.png")
()

Core functions and parameters:

• (): Histogram drawing function.
  • bins: Number of intervals or specific boundary values ​​(such asbins=[0, 10, 20]）。
  • color/edgecolor: Fill color and border color.
  • alpha: Transparency (0-1).
  • density: Whether to display frequency (normalization).
• ax.set_title()/ax.set_xlabel()/ax.set_ylabel(): Title and axis labels.

3. Use seaborn to draw histograms

seabornbased onmatplotlib, providing more concise syntax and statistical functions (such as kernel density estimation).

Code example:

import pandas as pd
import seaborn as sns
import  as plt
# Read Excel datadf = pd.read_excel("")
data = df["Numerical column"]
# Set the themesns.set_theme(style="whitegrid", font="SimHei")  # Chinese fonts need to be specified# Create a histogram(figsize=(10, 6))
ax = (
    data,
    bins=10,                # Number of intervals    kde=True,               # Show kernel density curve    color="skyblue",        # color    edgecolor="black",      # Border color    stat="count"            # Statistics type (default count, optional "density"))
# Add title and tagax.set_title("Numerical distribution histogram (core density estimation)", fontsize=14)
ax.set_xlabel("Numerical Range", fontsize=12)
ax.set_ylabel("Frequency", fontsize=12)
# Show chartsplt.tight_layout()
("seaborn_histogram.png")
()

Core functions and parameters:

• (): Histogram drawing function.
  • kde: Whether to display the kernel density estimation curve (defaultFalse）。
  • stat: Statistical type ("count"Frequency,"density"Frequency,"percent"Percentage).
  • bins/color/edgecolor: samematplotlib。
• sns.set_theme(): Set theme style (such aswhitegrid、darkgrid）。

Comparative summary

Things to note

• Data preprocessing: Make sure that the target column in Excel is of a numeric type (non-string).
• Interval division:AdjustmentbinsParameter optimization data distribution display (such asbins=20add details).
• Interactive：pyechartsFit to generate HTML files, andmatplotlib/seabornSuitable for static pictures.

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