BUG: read_excel surprisingly filling empty levels in MultiIndex after first value

[phofl]

• I have checked that this issue has not already been reported.

• I have confirmed this bug exists on the latest version of pandas.

• I have confirmed this bug exists on the master branch of pandas.

Reproducible Example

import pandas as pd
df = pd.DataFrame({"a": [1, 2, 3], "b": [np.nan, 1, np.nan], "c": 1})

df.to_excel("test.xlsx", index=False)

pd.read_excel("test.xlsx", index_col=[0, 1])

Issue Description

The code snippet returns

       c
a b     
1 NaN  1
2 1.0  1
3 1.0  1

Based on the discussions when this was implemented I think the filling of NaNs was implemented to handle merged cells, but in this case this is quite surprising and I think should be considered a bug.

Expected Behavior

I would expect:

       c
a b     
1 NaN  1
2 1.0  1
3 NaN  1

cc @rhshadrach I think you have done a few things with read_excel in the past?

Installed Versions

INSTALLED VERSIONS ------------------ commit : ff84f69 python : 3.8.12.final.0 python-bits : 64 OS : Linux OS-release : 5.11.0-41-generic Version : #45~20.04.1-Ubuntu SMP Wed Nov 10 10:20:10 UTC 2021 machine : x86_64 processor : x86_64 byteorder : little LC_ALL : None LANG : en_US.UTF-8 LOCALE : en_US.UTF-8

pandas : 1.4.0.dev0+1349.gff84f69269
numpy : 1.21.3
pytz : 2021.1
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : 0.29.24
pytest : 6.2.5
hypothesis : 6.23.1
sphinx : 4.2.0
blosc : None
feather : None
xlsxwriter : 3.0.1
lxml.etree : 4.6.3
html5lib : 1.1
pymysql : None
psycopg2 : None
jinja2 : 3.0.1
IPython : 7.28.0
pandas_datareader: None
bs4 : 4.10.0
bottleneck : 1.3.2
fsspec : 2021.11.0
fastparquet : 0.7.1
gcsfs : 2021.05.0
matplotlib : 3.4.3
numexpr : 2.7.3
odfpy : None
openpyxl : 3.0.9
pandas_gbq : None
pyarrow : 5.0.0
pyxlsb : None
s3fs : 2021.11.0
scipy : 1.7.2
sqlalchemy : 1.4.25
tables : 3.6.1
tabulate : 0.8.9
xarray : 0.18.0
xlrd : 2.0.1
xlwt : 1.3.0
numba : 0.53.1
None

[abatomunkuev]

Hello @phofl ! I would like to work on this issue.

[abatomunkuev]

@phofl I have done some root cause analysis and found a potential problem that causes filling missing values in read_excel() method.

I have found the cause of the problem in file: pandas/io/excel/_base.py

Specifically, in this portion of code:

pandas/pandas/io/excel/_base.py

Lines 608 to 618 in cbb6c73

	# Check if we have an empty dataset
	# before trying to collect data.
	if offset < len(data):
	for col in index_col:
	last = data[offset][col]
	for row in range(offset + 1, len(data)):
	if data[row][col] == "" or data[row][col] is None:
	data[row][col] = last
	else:
	last = data[row][col]

I have some questions regarding the portion of code above:

• What does the variable offset do ?
• In the loop below, why do we define the range from offset + 1 to length of the DataFrame.

• pandas/pandas/io/excel/_base.py

  Line 614 in cbb6c73

  for row in range(offset + 1, len(data)):

• Since we start with offset + 1, the iteration starts with the second row of the DataFrame. I have logged the row. It is starting from the second row of the DataFrame, skipping the first row containing the missing value (N/A).

• Since, it skipped the first row, it didn't find the first missing value (N/A)
• Secondly, could you explain the portion of code below?
  

  pandas/pandas/io/excel/_base.py

  Lines 608 to 618 in cbb6c73

  	# Check if we have an empty dataset
  	# before trying to collect data.
  	if offset < len(data):
  	for col in index_col:
  	last = data[offset][col]
  	for row in range(offset + 1, len(data)):
  	if data[row][col] == "" or data[row][col] is None:
  	data[row][col] = last
  	else:
  	last = data[row][col]

• Question is should we remove that portion of code to allow having missing values in read_excel function.
• After removing that portion of the code, the method read_excel() doesn't fill the missing values.
  

So my concerns are:

• Should we remove the following portion of code:

• pandas/pandas/io/excel/_base.py

  Lines 613 to 619 in cbb6c73

  	for row in range(offset + 1, len(data)):
  	if data[row][col] == "" or data[row][col] is None:
  	data[row][col] = last
  	else:
  	last = data[row][col]

• If we remove the portion of code above, what will be possible bad effects of it?
• Is there a way to not removing, just modifying that portion of code? Let me know, I can work on it with your assistance.

[abatomunkuev]

@phofl I will share Google Colab notebook, where I have been doing the root cause analysis.

https://colab.research.google.com/drive/1_TC3Vd4fE2xzEoUJbuqnxtsFeH2nQW0a?usp=sharing

[rhshadrach]

This behavior was introduced in #10967. pandas is forward filling index columns under the assumption that the excel sheet was written from a pandas MultiIndex. It seems we could write a function that tries to determine if a given set of columns arises this way. It won't ever able to be perfect. I think better would be to have it be user specified, but there are already an overwhelming number of arguments to read_excel.

[abatomunkuev]

@rhshadrach Thanks for clarification.

I am going to try to implement one of the mentioned ideas

[abatomunkuev]

@rhshadrach @phofl

I have found out that the variable last gets overridden when the current MultiIndex cell is not empty (not N/A).

pandas/pandas/io/excel/_base.py

Lines 614 to 618 in cbb6c73

	for row in range(offset + 1, len(data)):
	if data[row][col] == "" or data[row][col] is None:
	data[row][col] = last
	else:
	last = data[row][col]

Consider the DataFrame that was provided to trigger the issue by @phofl. Suppose that columns a and b are MultiIndexes.

Once the iteration reaches the second row of the B index, it will override the last variable with the value of 1. After that, it finds the third row of the B index has a missing value (N/A), it assigns the value of the variable last to it. Hence, the missing value fills with 1.

To possibly solve this issue, can we add an argument to the read_excel() method so that it will not override Indexes with N/A?

@rhshadrach Could you please clarify this sentence:
It seems we could write a function that tries to determine if a given set of columns arises this way. It won't ever able to be perfect. #44837 (comment)

In which way does the specified set of columns arise? in case we will choose to implement this function.

[phofl]

I am not sure if we want to add an argument to read_excel for this, an easy workaround would be to simply call set_index afterwards.

Implementing a function guessing this sounds nice, but not sure if we can do this in a way to avoid inconsistencies?

[abatomunkuev]

@phofl How we would call the set_index() method, if we already specified the MultiIndexes using the argument index_col?

Do you mean that we could use set_index() method inside the read_excel() method?

[phofl]

No after calling read_excel

[abatomunkuev]

The method chaining works.

I think we still need to solve the issue with index_col

[rhshadrach]

@rhshadrach Could you please clarify this sentence: It seems we could write a function that tries to determine if a given set of columns arises this way. It won't ever able to be perfect. #44837 (comment)

In which way does the specified set of columns arise? in case we will choose to implement this function.

The following two sheets are the same (ignoring formatting)

df = pd.DataFrame({'a': [1., 1., 2.], 'b': [4, 5, 6], 'c': [7, 8, 9]}).set_index(list('ab'))
df.to_excel('test.xlsx')

df = pd.DataFrame({'a': [1., np.nan, 2.], 'b': [4, 5, 6], 'c': [7, 8, 9]})
df.to_excel('test2.xlsx', index=False)

so it is impossible to tell (at least, using cell values) whether a frame was written with a MultiIndex and therefore blanks should be filled in, or if the blanks correspond to null values.

[rhshadrach]

-1 on adding an argument to excel for this. Instead, I think we should enable it to be easily accomplished. First, the behavior of read_excel should be changed to not ffill: ffill is not invertible. It is then up to the user whether they need to apply ffill themselves. Now, Index does not have a ffill, but if it did, I think we could do something like

df = pd.read_excel("test.xlsx", index_col=[0, 1])
df.index = df.index.ffill()  # for when blank cells are from writing a MultiIndex rather than null values