Python图表绘制工具：Matplotlib_Part 3

####【课程3.13】 表格样式创建     表格视觉样式：Dataframe.style → 返回pandas.Styler对象的属性，具有格式化和显示Dataframe的有用方法

样式创建： ① Styler.applymap：elementwise → 按元素方式处理Dataframe ② Styler.apply：column- / row- / table-wise → 按行/列处理Dataframe

#####样式

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
% matplotlib inline

# 样式

df = pd.DataFrame(np.random.randn(10, 4), columns = ["a", "b", "c", "d"])
sty = df.style
print(sty, type(sty))

sty
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def color_neg_red(var): if var < 0: color = "red" else: color = "black" return("color:%s" %color) df.style.applymap(color_neg_red)

创建样式方法，使得小于0的数变成红色

style.applymap() → 自动调用其中的函数

![2.png](https://upload-images.jianshu.io/upload_images/2330091-4ce4a02861e6d2cf.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)

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>#####按行/列处理样式：style.apply()
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按行/列处理样式：style.apply()

def hightlight_max(s): is_max = s == s.max()

print("##",is_max)

lst = []
for v in is_max:
    if v:
        lst.append("background-color: yellow")
    else:
        lst.append("")
return(lst)
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df.style.apply(hightlight_max, axis = 0, subset = ["b", "c"])

创建样式方法，每列最大值填充黄色

axis：0为列，1为行，默认为0

subset：索引

![3.png](https://upload-images.jianshu.io/upload_images/2330091-805a4ea316c44dde.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)
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>####样式索引、切片

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样式索引、切片

df.style.apply(hightlight_max, axis = 1, subset = pd.IndexSlice[2:5, ["b", "d"]])

通过pd.IndexSlice[]调用切片

也可：df[2:5].style.apply(hightlight_max, subset = ["b", "d"]) -> 先索引行再做样式

![4.png](https://upload-images.jianshu.io/upload_images/2330091-3c0a5dde53768718.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)

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####【课程3.14】  表格显示控制
&ensp;&ensp;&ensp;&ensp;df.style.format()

>##### 按照百分数显示
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按照百分数显示

df = pd.DataFrame(np.random.randn(10, 4), columns = ["a", "b", "c", "d"]) print(df.head()) df.head().style.format("{:.2%}")

![5.png](https://upload-images.jianshu.io/upload_images/2330091-95819a71b09ddebe.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)


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>##### 显示小数点数
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显示小数点数

df.head().style.format("{:.4f}")

![6.png](https://upload-images.jianshu.io/upload_images/2330091-d4917404a752a509.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)

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>##### 显示正负数
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显示正负数

df.head().style.format("{:+.2f}")

![7.png](https://upload-images.jianshu.io/upload_images/2330091-a70f59bf8b74f83e.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)

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>####分列显示


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分列显示

df.head().style.format({"b": "{:.2%}", "c": "{:+.3f}", "d": "{:.3f}"})

![8.png](https://upload-images.jianshu.io/upload_images/2330091-65e5adbdf7595c22.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)

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####【课程3.15】  表格样式调用
&ensp;&ensp;&ensp;&ensp;Styler内置样式调用
>#####定位空值
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定位空值

df = pd.DataFrame(np.random.rand(5,4), columns = list("ABCD")) df["A"][2] = np.nan df.style.highlight_null(null_color = "red")

![9.png](https://upload-images.jianshu.io/upload_images/2330091-fbef8e1d7fe472a6.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)


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>#####色彩映射
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色彩映射

df = pd.DataFrame(np.random.rand(10, 4), columns = list("ABCD")) df.style.background_gradient(cmap = "Greens", axis = 1, low = 0, high = 1)

cmap：颜色

axis：映射参考，0为行，1以列

![10.png](https://upload-images.jianshu.io/upload_images/2330091-8622059eaf448f36.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)


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>##### 条形图
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条形图

df = pd.DataFrame(np.random.rand(10, 4), columns = list("ABCD")) df.style.bar(subset = ["A", "B", "C"], color = "#d65f5f", width = 100)

width：最长长度在格子的占比

![11.png](https://upload-images.jianshu.io/upload_images/2330091-df6aae785c3281.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)

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>####分段式构建样式

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分段式构建样式

df = pd.DataFrame(np.random.rand(10, 4), columns = list("ABCD")) df["A"][[3,2,1]] = np.nan df.style.bar(subset = ["A", "B"], color = "#d65f5f", width = 100).highlight_null(null_color = "yellow")

![12.png](https://upload-images.jianshu.io/upload_images/2330091-0210cbd6b240a599.png?imageMogr2/auto-orient/strip%7CimageView2/2/w/1240)


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#####最后：
[以上完整代码](https://github.com/ChaoRenYuan/Python/tree/master/Python数据分析工具/图表绘制工具：Matplotlib)
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