1五、表格样式调用

时间 2019-11-16

标签表格样式调用繁體版

原文原文链接

In [ ]:

'''
表格样式调用

Styler内置样式调用

'''

In [1]:

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

In [6]:

# 定位空值

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

          A         B         C         D
0  0.900156  0.173128  0.464474  0.767169
1  0.696482  0.152322  0.748511  0.033772
2  0.692276  0.922752  0.891984  0.251889
3  0.158484  0.022629  0.531354  0.903283
4  0.088670  0.663002  0.081860  0.425972

Out[6]:

	A	B	C	D
0	0.900156	0.173128	0.464474	0.767169
1	0.696482	0.152322	0.748511	0.0337716
2	nan	0.922752	0.891984	0.251889
3	0.158484	0.0226286	0.531354	0.903283
4	0.0886705	0.663002	0.0818596	0.425972

In [10]:

# 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

Out[10]:

	A	B	C	D
0	0.732847	0.0496179	0.703522	0.0831114
1	0.114995	0.817112	0.189721	0.716026
2	0.856876	0.0892837	0.396583	0.684451
3	0.235094	0.90865	0.818304	0.210525
4	0.249924	0.610012	0.290704	0.899304
5	0.873552	0.792285	0.381289	0.68927
6	0.8235	0.611679	0.680009	0.441089
7	0.0990742	0.51448	0.688829	0.31839
8	0.45883	0.270769	0.0175723	0.440186
9	0.93711	0.488835	0.35806	0.0399387

In [14]:

# 条形图

df = pd.DataFrame(np.random.rand(10,4),columns = list('ABCD'))
df['B'][0] = 1
df.style.bar(subset=['A', 'B'], color='#d65f5f', width=100)
# width：最长长度在格子的占比

Out[14]:

	A	B	C	D
0	0.979648	1	0.253707	0.00287105
1	0.064052	0.834327	0.307131	0.449442
2	0.798576	0.803261	0.0931021	0.746322
3	0.060138	0.0757324	0.497424	0.871116
4	0.2484	0.106362	0.995205	0.913493
5	0.961849	0.873627	0.540741	0.134807
6	0.116295	0.643925	0.36211	0.325609
7	0.719927	0.665219	0.378141	0.364791
8	0.557872	0.701442	0.607912	0.836915
9	0.0725032	0.188093	0.593798	0.181025

In [5]:

# 分段式构建样式

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

Out[5]:

	A	B	C	D
0	0.0513568	0.124323	0.607603	0.325538
1	0.905717	0.673226	0.347249	0.253652
2	nan	0.55414	0.522631	0.37466
3	nan	0.64204	0.318888	0.940745
4	0.873626	0.168051	0.330857	0.0659539
5	0.90829	0.689768	0.694224	0.343079
6	0.932276	0.138928	0.689567	0.664704
7	0.400204	0.677357	0.0898043	0.89703
8	0.74028	0.892482	0.565825	0.892374
9	0.852043	0.848985	0.502153	0.163207