Description
Code Sample, a copy-pastable example if possible
import pandas as pd
import numpy as np
np.random.seed(0)
a = np.random.randint(1, 4, 10)
b = np.random.randint(1, 4, 10)
c = np.random.randint(5, 9, 10)
df = pd.DataFrame(dict(A=a,B=b,C=c,Val=np.random.randint(1,10,10)))
tbl=pd.pivot_table(df,index=['A','B','C'], aggfunc=sum)
print tbl.to_html()
print tbl.style.render()
Expected Output
Expect that both tables will produce nearly indentical hierarchically structured tables, and in particular, the second table will not have every index value repeated.
`
| Val | |||
|---|---|---|---|
| A | B | C | |
| 1 | 1 | 5 | 1 |
| 2 | 5 | 4 | |
| 6 | 4 | ||
| 8 | 9 | ||
| 2 | 3 | 5 | 2 |
| 6 | 3 | ||
| 8 | 9 | ||
| 3 | 1 | 8 | 4 |
| 2 | 7 | 4 |
<style type="text/css" >
</style>
<table id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" None>
<thead>
<tr>
<th class="blank">
<th class="blank">
<th class="blank">
<th class="col_heading level0 col0">Val
</tr>
<tr>
<th class="col_heading level2 col0">A
<th class="col_heading level2 col1">B
<th class="col_heading level2 col2">C
<th class="blank">
</tr>
</thead>
<tbody>
<tr>
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level2 row0">
1
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level2 row0">
1
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level2 row0">
5
<td id="T_ae86631e_1789_11e6_aa5c_3417ebaea714row0_col0" class="data row0 col0">
1
</tr>
<tr>
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row1">
1
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row1">
2
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row1">
5
<td id="T_ae86631e_1789_11e6_aa5c_3417ebaea714row1_col0" class="data row1 col0">
4
</tr>
<tr>
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row2">
1
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row2">
2
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row2">
6
<td id="T_ae86631e_1789_11e6_aa5c_3417ebaea714row2_col0" class="data row2 col0">
4
</tr>
<tr>
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row3">
1
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row3">
2
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row3">
8
<td id="T_ae86631e_1789_11e6_aa5c_3417ebaea714row3_col0" class="data row3 col0">
9
</tr>
<tr>
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row4">
2
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row4">
3
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row4">
5
<td id="T_ae86631e_1789_11e6_aa5c_3417ebaea714row4_col0" class="data row4 col0">
2
</tr>
<tr>
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row5">
2
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row5">
3
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row5">
6
<td id="T_ae86631e_1789_11e6_aa5c_3417ebaea714row5_col0" class="data row5 col0">
3
</tr>
<tr>
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row6">
2
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row6">
3
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row6">
8
<td id="T_ae86631e_1789_11e6_aa5c_3417ebaea714row6_col0" class="data row6 col0">
9
</tr>
<tr>
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row7">
3
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row7">
1
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row7">
8
<td id="T_ae86631e_1789_11e6_aa5c_3417ebaea714row7_col0" class="data row7 col0">
4
</tr>
<tr>
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row8">
3
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row8">
2
<th id="T_ae86631e_1789_11e6_aa5c_3417ebaea714" class="row_heading level0 row8">
7
<td id="T_ae86631e_1789_11e6_aa5c_3417ebaea714row8_col0" class="data row8 col0">
4
</tr>
</tbody>
</table>`
output of pd.show_versions()
INSTALLED VERSIONS
commit: None
python: 2.7.11.final.0
python-bits: 32
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None
pandas: 0.18.1
nose: None
pip: 8.1.1
setuptools: 20.2.2
Cython: None
numpy: 1.11.0
scipy: None
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: None
patsy: None
dateutil: 2.5.3
pytz: 2016.4
blosc: None
bottleneck: None
tables: None
numexpr: None
matplotlib: None
openpyxl: None
xlrd: None
xlwt: 1.0.0
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.8
boto: None
pandas_datareader: 0.2.1