Python 基于多种条件设计熊猫?
我想根据2-3个条件为一些行着色: df 我想根据所有三列填充背景 i、 e 我不认为我能够理解样式如何工作的概念。有人能举例说明一下吗Python 基于多种条件设计熊猫?,python,pandas,Python,Pandas,我想根据2-3个条件为一些行着色: df 我想根据所有三列填充背景 i、 e 我不认为我能够理解样式如何工作的概念。有人能举例说明一下吗 提前谢谢 您可以使用loc创建样式的数据框,并按条件设置行: 非常感谢。你是最有价值球员。 status days_since_claim claim_action 0 Closed 349 days No action 1 Closed 353 days No action
提前谢谢 您可以使用loc创建样式的数据框,并按条件设置行:
非常感谢。你是最有价值球员。
status days_since_claim claim_action
0 Closed 349 days No action
1 Closed 353 days No action
2 Granted 373 days Check account
3 Granted 431 days Account checked
4 Closed 448 days No action
`backgroud_color: 'green' if 'status' == 'Closed' and claim_action == 'No action'
`backgroud_color: 'red' if 'status' == 'Granted' and claim_action == 'Check account' and 'days_since_claim' > 300`
I tried:
styled = mdf.style.applymap(lambda v: 'background-color: %s' %
'red' if v > 300 else "")
def color_s(df):
for i, row in df.iterrows():
if row['status'] == 'Closed':
.
.
def color(x):
c1 = 'background-color: green'
c2 = 'background-color: red'
c = ''
#compare columns
mask1 = (x['status'] == 'Closed') &
(x['claim_action'] == 'No action')
mask2 = (x['status'] == 'Granted') &
(x['claim_action'] == 'Check account') &
(x['days_since_claim'].dt.days > 300)
#DataFrame with same index and columns names as original filled empty strings
df1 = pd.DataFrame(c, index=x.index, columns=x.columns)
#modify values of df1 column by boolean mask
df1.loc[mask1, :] = c1
df1.loc[mask2, :] = c2
return df1
df.style.apply(color, axis=None)