我希望在 Jupyter 笔记本中的 pandas 数据框中复制下面的图像。 2020年订单标题不需要。我发现了这个页面https://github.com/crdietrich/sparklines/blob/master/Pandas%20Sparklines%20Demo.ipynb,它似乎在数据框中添加了迷你图,但不是条形图。任何见解将不胜感激!下面的代码在数据框列中提供了迷你图,但如图所示,我希望使用以下示例数据堆叠条形图。
# example data
data = [[20, 10], [50, 15], [6, 14]]
# Create the pandas DataFrame
df = pd.DataFrame(data, columns=['Orchid', 'Rose'])
# print dataframe.
print(df)
我正在寻找的输出应该类似于下图。
在列中显示迷你图,但不显示堆叠条形的示例代码。
import numpy as np
import pandas as pd
from scipy import stats
import matplotlib.pyplot as plt
%matplotlib inline
import sparklines
# Create some data
density_func = 78
mean, var, skew, kurt = stats.chi.stats(density_func, moments='mvsk')
x_chi = np.linspace(stats.chi.ppf(0.01, density_func),
stats.chi.ppf(0.99, density_func), 100)
y_chi = stats.chi.pdf(x_chi, density_func)
x_expon = np.linspace(stats.expon.ppf(0.01), stats.expon.ppf(0.99), 100)
y_expon = stats.expon.pdf(x_expon)
a_gamma = 1.99
x_gamma = np.linspace(stats.gamma.ppf(0.01, a_gamma),
stats.gamma.ppf(0.99, a_gamma), 100)
y_gamma = stats.gamma.pdf(x_gamma, a_gamma)
n = 100
np.random.seed(0) # keep generated data the same for git commit
data = [np.random.rand(n),
np.random.randn(n),
np.random.beta(2, 1, size=n),
np.random.binomial(3.4, 0.22, size=n),
np.random.exponential(size=n),
np.random.geometric(0.5, size=n),
np.random.laplace(size=n),
y_chi,
y_expon,
y_gamma]
function = ['rand',
'randn',
'beta',
'binomial',
'exponential',
'geometric',
'laplace',
'chi',
'expon',
'gamma']
df = pd.DataFrame(data)
df['function'] = function
df
# Define range of data to make sparklines
a = df.ix[:, 0:100]
# Output to new DataFrame of Sparklines
df_out = pd.DataFrame()
df_out['sparkline'] = sparklines.create(data=a)
sparklines.show(df_out[['sparkline']])
# Insert Sparklines into source DataFrame
df['sparkline'] = sparklines.create(data=a)
sparklines.show(df[['function', 'sparkline']])
# Detailed Formatting
df_out = pd.DataFrame()
df_out['sparkline'] = sparklines.create(data=a,
color='#1b470a',
fill_color='#99a894',
fill_alpha=0.2,
point_color='blue',
point_fill='none',
point_marker='*',
point_size=3,
figsize=(6, 0.25))
sparklines.show(df_out[['sparkline']])
# Example Data and Sparklines Layout
df_copy = df[['function', 'sparkline']].copy()
df_copy['value'] = df.ix[:, 100]
df_copy['change'] = df.ix[:,98] - df.ix[:,99]
df_copy['change_%'] = df_copy.change / df.ix[:,99]
sparklines.show(df_copy)
您可以使用 Styler 进行调整,使用 hacky
bar
:
st = (
df.assign(
bar=df["Orchid"].fillna(df["Rose"]),
tmp=df["Orchid"] + df["Rose"],
)
.style.set_caption("Orders in 2020")
.bar(subset=["bar", "tmp"], color="#fb9c04", axis=1)
.set_table_styles(
[
{
"selector": "td.col2",
"props": "background-color: #668ed2"
},
{
"selector": "caption",
"props": "font-size: large; font-weight: bold",
},
],
)
.hide(subset="tmp", axis=1)
.format("", na_rep="", subset="bar")
.format("${:,.2f}", na_rep="No Data", subset=["Orchid", "Rose"])
.format_index(lambda c: c if c != "col" else "", axis=1)
.map_index(lambda i: "font-weight: bold")
.format_index(lambda i: i + 1)
)
输出:
使用的输入(
df
):
df = pd.DataFrame(
{
"Orchid": [
3500.0, 4800.0, 1400.0, None, 7800.0, 6800.0,
8500.0, 6200.0, 9000.0, 7300.0, 8300.0, 11300.0,
],
"Rose": [
3200.0, 2500.0, 3700.0, 5600.0, 8000.0, 3800.0,
None, 7720.0, 8380.0, 9100.0, 9700.0, 10360.0,
],
}
)