Recently I discovered a library ( plottable) on github that can set up fancy DataFramestyles in a simple way.
Address on github: https://github.com/znstrider/plottable
1. Installation
Install via pip:
pip install plottable
2. Row color
Using plottablethe API, it is very convenient to adjust the background and font color.
2.1. Different colors for odd and even rows
Set different colors for odd and even rows to make the table look layered.
import numpy as np
from plottable import Table
data = np.random.random((5, 5))
data = data.round(2)
df = pd.DataFrame(data, columns=["A", "B", "C", "D", "E"])
tbl = Table(df,
odd_row_color="#f0f0f0",
even_row_color="#e0f6ff"
)
2.2. Background and font color
For complex display requirements, the background color and font color can be set line by line.
import numpy as np
from plottable import Table
data = np.random.random((5, 5))
data = data.round(2)
df = pd.DataFrame(data, columns=["A", "B", "C", "D", "E"])
tbl = Table(df)
tbl.rows[0].set_facecolor("red")
tbl.rows[0].set_fontcolor("white")
tbl.rows[1].set_facecolor("blue")
tbl.rows[1].set_fontcolor("white")
tbl.rows[2].set_facecolor("green")
tbl.rows[2].set_fontcolor("white")
tbl.rows[3].set_facecolor("gray")
tbl.rows[3].set_fontcolor("white")
tbl.rows[4].set_facecolor("purple")
tbl.rows[4].set_fontcolor("white")
In the above example, the background of each row is set to a different color, and the font is set to white.
3. Display of values
Adjusting colors and fonts are basic settings. plottableThe power is that the data can be displayed graphically,
allowing us to see the size and gaps of the data at a glance.
For example, the following example ColumnDefinitionuses plottablethe built-in data display.
import numpy as np
from matplotlib.colors import LinearSegmentedColormap
from plottable import ColumnDefinition, Table
from plottable.formatters import decimal_to_percent
from plottable.plots import bar, percentile_bars, percentile_stars, progress_donut
data = np.random.random((5, 5))
data = data.round(2)
df = pd.DataFrame(data, columns=["A", "B", "C", "D", "E"])
print(df) # 显示原始数据
cmap = LinearSegmentedColormap.from_list(
name="bugw", colors=["#ffffff", "#f2fbd2", "#c9ecb4", "#93d3ab", "#35b0ab"], N=256
)
tab = Table(
df,
textprops={
"ha": "center"},
column_definitions=[
ColumnDefinition("index", textprops={
"ha": "left"}),
ColumnDefinition("A", plot_fn=percentile_bars, plot_kw={
"is_pct": True}),
ColumnDefinition(
"B", width=1.5, plot_fn=percentile_stars, plot_kw={
"is_pct": True}
),
ColumnDefinition(
"C",
plot_fn=progress_donut,
plot_kw={
"is_pct": True, "formatter": "{:.0%}"},
),
ColumnDefinition(
"D",
width=1.25,
plot_fn=bar,
plot_kw={
"cmap": cmap,
"plot_bg_bar": True,
"annotate": True,
"height": 0.5,
"lw": 0.5,
"formatter": decimal_to_percent,
},
),
],
)
Raw data shows:
plottableAfter strengthening, it shows:
4. Mixing graphics and text
Finally, an plottableexample of inserting a picture into a table is shown.
The data source is the data of each team in the 2023 Honor of Kings Spring Split .
The main purpose is to insert pictures into the demonstration table (the pictures are the logos of each team), so only 4 columns are selected for display.
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
from plottable import ColumnDefinition, Table
from plottable.formatters import decimal_to_percent
from plottable.plots import bar, percentile_bars, percentile_stars, progress_donut
from plottable.plots import circled_image
matplotlib.rcParams["font.sans-serif"] = ["Microsoft YaHei Mono"]
matplotlib.rcParams["axes.unicode_minus"] = False
df = pd.read_csv("d:/share/data.csv")
df = df.set_index("排名")
df["胜率"] = df["胜场"] / df["比赛场次"]
df["logo"] = "d:/share/wzry-logos/" + df["战队"] + ".png"
df = df.drop(columns=["胜场", "比赛场次", "场均KDA"])
fig, ax = plt.subplots(figsize=(12, 12))
col_defs = [
ColumnDefinition("排名", textprops={
"ha": "left"}),
ColumnDefinition(
name="logo",
title="",
textprops={
"ha": "center"},
width=0.5,
plot_fn=circled_image,
),
ColumnDefinition("战队", textprops={
"ha": "center"}),
ColumnDefinition(
"胜率",
plot_fn=progress_donut,
plot_kw={
"is_pct": True, "formatter": "{:.0%}"},
),
]
tbl = Table(
df,
ax=ax,
textprops={
"ha": "center", "fontsize": 20},
column_definitions=col_defs,
)
The data and logo icon used in the above example are shared at:
https://url11.ctfile.com/f/45455611-870642180-a094e4?p=6872 (access password: 6872)
有兴趣可以试试看上面的示例，或者继续深入探索 plottable 的强大显示功能。
本文由 mdnice 多平台发布