[英]pandas multiindex column styler
Versions: Python 3.7.6
, pandas 1.0.0
版本:
Python 3.7.6
, pandas 1.0.0
Input dataframe输入数据框
df = pd.DataFrame(dict(
recruit_dt=["1/1/2017"]*3+["1/1/2018"]*3+["1/1/2019"]*3,
label = [1,3,4]*3,
nmem = np.random.choice(list(range(10000,3000000)),9),
pct_fem = np.random.sample(9),
mean_age = 50 + 10*np.random.sample(9),
sd_age = 8 + 2*np.random.sample(9)
))
Would like to present this after the following transformations想在以下转换后呈现这个
dfp = pd.pivot_table(df, values=["nmem","pct_fem","mean_age","sd_age"], index="recruit_dt", columns="label")
dfp = dfp.reindex(columns=['nmem', 'pct_fem', 'mean_age', 'sd_age'], level=0)
How do I write the styler so that all the nmem
columns have thousand separators {:,}
, 'pct_fem' are percentages to two decimal places, mean_age
and sd_age
are floating point numbers with two decimal places?如何编写样式器以便所有
nmem
列都有千位分隔符{:,}
,'pct_fem' 是两位小数的百分比, mean_age
和sd_age
是两位小数的浮点数? Is there an approach which uses styler.format
or styler.apply
with IndexSlice
?是否有将
styler.format
或styler.apply
与IndexSlice
styler.apply
使用的IndexSlice
?
== EDIT: this seems to work. == 编辑:这似乎有效。 Is there a more concise solution?
有没有更简洁的解决方案?
dfp.columns.names = ["metrics","label"]
dfp.style.format("{:,}", subset=pd.IndexSlice[:,'nmem']) \
.format("{:.2%}", subset=pd.IndexSlice[:,'pct_fem']) \
.format("{:.2f}", subset=pd.IndexSlice[:,['mean_age','sd_age']])
You can specify an argument to the subset
parameter using a list comprehension to select the relevant columns.您可以使用列表理解为
subset
参数指定参数以选择相关列。
>>> (dfp
.style
.format('{:.0f}', na_rep='-', subset=[col for col in dfp.columns if col[0] == 'nmen'])
.format('{:.2%}', na_rep='-', subset=[col for col in dfp.columns if col[0] == 'pct_fem'])
.format('{:,.2f}', na_rep='-', subset=[col for col in dfp.columns if col[0] in {'mean_age', 'sd_age'}])
)
A more general solution:更通用的解决方案:
# Styles.
pct_two = '{:.2%}'
comma_float = '{:.0f}'
comma_float_2 = '{:.2f}'
# Styling to be applied to specified columns.
formats = {
'nmean': comma_float,
'pct_fem': pct_two,
'mean_age': comma_float_2,
'sd_age': comma_float_2,
}
# Create dictionary of multi-index columns with specified styling.
format_dict = {
midx: formats[level_val]
for level_val in formats
for midx in [col for col in dfp if col[0] == level_val]
}
# Apply styling to dataframe.
dfp.style.format(format_dict)
Let's try this:让我们试试这个:
idx = pd.IndexSlice
formatter_dict = {i:"{:,}" for i in dfp.loc[:, idx['nmem', :]].columns}
formatter_dict2 = {i:"{:.2%}" for i in dfp.loc[:, idx['pct_fem', :]].columns}
formatter_dict3 = {i:"{:.2f}" for i in dfp.loc[:, idx[['mean_age', 'sd_age'], :]].columns}
formatter_dict.update(formatter_dict2)
formatter_dict.update(formatter_dict3)
dfp.style.format(formatter_dict)
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