I tried a fairly simple prompt using Ollama and DeepSeek's R1 8B model. The result was a pretty hilarious example of a model spinning out in an endless loop while considering alternatives. I eventually had to kill it with CTRL-C.

1-prompt.txt

>>> I'm loading a spreadsheet in pandas. What's the best way to find the first n
... on empty column and row to consider the "table" that will eventually become
... the dataframe? Write a method to do this `_get_dataframe_from_excel(sheet_na
... me: str, excel_file: ExcelFile)`

2-deepseek-r1:8b-response.txt

<think>
Okay, I need to help the user solve their problem of loading a spreadsheet into pandas. They're asking for the best way to find the first non-empty column and row to consider
as the table that becomes a DataFrame. The method they want is _get_dataframe_from_excel(sheet_name: str, excel_file: ExcelFile).

Let me think about how to approach this. So, when you load an Excel file into pandas using pd.read_excel(), it returns a DataFrame. But sometimes, sheets might have empty
columns or rows at the top that we don't need. We want to skip these and get the first meaningful table.

First, I should open the Excel file using pandas.ExcelFile. That makes sense because it allows iterating over sheets easily. Then, for each sheet name in the file, if the
sheet matches the given sheet_name, read the data into a DataFrame.

But wait, some sheets might have headers or leading empty rows that aren't needed. So, the plan is: iterate through each row and column of the DataFrame until we find the
first non-empty cell. Once found, determine the minimal bounding box around this cell to define the sub DataFrame.

Hmm, how do I efficiently check for the first non-empty cell? Maybe using a loop where I skip rows until a non-empty value is found in any column. Once a non-empty row is
found, then check each column of that row to see which column isn't empty. That gives me the starting point.

Wait, but sometimes the data might have multiple columns with non-empty values, but maybe only some are present. So perhaps I should find the first row where at least one
value isn't null or empty. Then, within that row, find the leftmost and rightmost non-empty columns to define the DataFrame's columns.

But what about rows below? There might be more data after that. But the goal is just to extract up to the point where the data ends. So perhaps using iloc to get the first
index where a non-null value exists, then expanding from there.

I think I should also handle cases where all cells are empty or the sheet is completely empty. In such cases, return an empty DataFrame.

So putting this into steps:

1. Open the Excel file and select the specific sheet.
2. Read the data as a DataFrame, but without the index, since we'll determine it ourselves.
3. Iterate through each row to find the first one where any value is not null or empty (considering NaN and empty strings).
4. Once the first non-empty row is found, iterate through its columns to find the first non-empty column.
5. Then, expand that row index to the last non-empty row in the DataFrame, again checking each cell until all rows are considered.
6. Within those rows, determine the leftmost and rightmost columns with data.
7. Slice the DataFrame from the top row to the last row, and from the first column to the last column found.

Wait, but perhaps a better approach is to use the 'dropna' method on axes and then find the first non-empty cell. Let me think about that.

Alternatively, I can loop through each row, starting from 0, and for each row, check if any value in that row is not null or empty. Once such a row is found, that's the
start_row. Then, from that row, check each column to find the first non-empty cell, which gives us the start_col. Then, determine the end of the DataFrame by finding the last
row with data and the last column with data.

Yes, that makes sense. So in code:

- Read the Excel sheet into a DataFrame.
- Reset the index so we can iterate from 0.
- Iterate through each row index until a non-null value is found.
- Once start_row is found, iterate through that row's columns to find the first non-empty column.
- Then find the last row where any cell is not null, which gives end_row.
- Similarly, for columns, find the maximum column where data exists beyond start_col.

Then slice the DataFrame from start_row to end_row and from start_col to max_col.

I should also handle cases where the entire sheet is empty. So I need checks to avoid index errors.

Another thing: when using pd.read_excel(), if the first few rows are empty, but later on there's data, we want to skip those leading empty rows.

Wait, perhaps it's more efficient to use a loop over the DataFrame's columns and rows until a non-empty cell is found. But I need to ensure that this doesn't miss any cells
due to NaN or empty strings.

I should also consider how pandas handles different types of missing values. Maybe using df.dropna(how='all') could help, but perhaps it's better to find the first occurrence
manually.

Let me outline the code steps:

1. Import pandas and ExcelFile.
2. Open the Excel file and select the sheet.
3. Read data into a DataFrame, maybe with header=None to avoid assuming headers.
4. Check if the DataFrame is empty; if so, return an empty DataFrame.
5. Iterate over each row starting from 0:
   - For each row, check if any value in that row is not NaN and not an empty string (considering different conditions for missing values).
   - Once such a row is found, break the loop to get start_row.
6. Now, from this start_row, find the first column where the cell is not NaN or empty. That gives start_col.
7. Then, find the last non-empty row by iterating from start_row down to 0 until all rows are checked, but perhaps a better way is to reverse and find the first occurrence of
non-empty in reversed order.
8. Similarly for columns: after finding start_col, go up to column 0 and see which is the last one with data beyond start_col.

Wait, maybe an alternative approach: after determining start_row, set the DataFrame's index to start from that row, then find the first column where any value in those rows is
non-null. Then, reset the index again.

Alternatively, use the pandas methods like find() or rfind() on the DataFrame to get the bounds.

Wait, perhaps using the dropna method and then checking if the resulting DataFrame is empty. For example:

- After reading the data into df, do df.dropna(how='all', inplace=True).
- If df is not empty, proceed. Else, return empty.

But how does that work with header information? Maybe it's better to read without headers first, and then determine the start from there.

Alternatively, perhaps I can try reading the sheet with header=None, which gives a DataFrame with index as row numbers and columns as per Excel's headers.

Wait, but if the user has specific headers, using header=True might be necessary. Hmm, but in this case, we're trying to find the first data table, so maybe headers are not
needed or could interfere. So perhaps reading without assuming headers is better.

So:

df = pd.read_excel(excel_file, sheet_name=sheet_name, header=None)

Then, process df to find the start_row and columns.

But if the first column has headers (like 'Col1', 'Col2'), then we might not want that. So maybe processing without assuming headers is better.

Wait, but in this case, since we're trying to extract a table, perhaps the first row after some empty rows has meaningful data, which could be headers or data. But if we don't
know, it's safer to treat them as data.

So moving forward:

Read df = pd.read_excel(..., header=None)

Then, dropna on axis=1 to remove any NaN columns.

But perhaps I should loop through each row and column until a cell is found that isn't NaN or empty.

Wait, here's an approach: for each row index in df.index:

   - Check if any value in that row is not NaN and not empty.
   - If yes, set start_row = current index, break the loop.

Once start_row is found, then find the first column where the cell is not NaN or empty.

Then, expand from there to get the last non-empty row and last column.

Alternatively, once we have start_row, we can check each column from 0 onwards until a non-null value is found. That gives us start_col.

Similarly, for end_col: after finding start_col, go through all columns beyond start_col (from start_col to len(df.columns)-1) and find the last one that has any non-null
values in the rows from start_row onward.

Alternatively, perhaps using df[df.dropna(how='all')] would give a DataFrame where only rows with data remain. Then, check the first column of this new DataFrame to find the
earliest non-empty cell.

Wait, but if we dropna on how='all', any row without any non-null cells will be removed. So that gives us start_row as 0 (since it's the first remaining row). Then, within
that row, find the first non-null column.

Similarly, for end_col: look at all columns in the DataFrame and find the last one where any row from start_row has a non-null value.

This seems efficient. So:

df = pd.read_excel(...)
df = df.dropna(how='all')  # Remove entire rows with no data
if df.empty:
    return df

start_col = 0
for col in range(len(df.columns)):
    if df.iloc[start_row][col] is not NaN and not pd.isna(df.iloc[start_row][col]):
        start_col = col
        break

But wait, perhaps a better way:

df = df.dropna(how='all')
if df.empty:
    return df

start_row_idx = 0
for row in range(len(df)):
    if any(value is not np.nan for value in df.iloc[row]):
        start_row_idx = row
        break

Then, for columns:

start_col = 0
while start_col < len(df.columns) and (df.iloc[start_row_idx][start_col] == '' or pd.isna(df.iloc[start_row_idx][start_col])):
    start_col +=1

if start_col >= len(df.columns):
    return df  # no data found

end_col = 0
for col in reversed(range(len(df.columns))):
    if any(pd.notna(df.iloc[row][col]) for row in range(start_row, len(df))):
        end_col = col
        break

But wait, that might not be the correct approach. Perhaps a better way is to use df.dropna on columns.

Alternatively, since after dropping rows with no data (how='all'), any remaining column could have headers or data. So perhaps:

start_col = 0
for col in range(len(df.columns)):
    if not df.iloc[start_row_idx].isnull()[col]:
        start_col = col
        break

But this assumes that the first non-null cell in the start_row is a header.

Hmm, this seems tricky.

Wait, maybe I should just use df.index and columns to find the earliest non-null cell after dropping rows with no data.

So:

df = pd.read_excel(...)
df = df.dropna(how='all')  # drop rows with all NaN
if df.empty:
    return df

# Find start_row: first row with at least one non-NaN value in any column
start_row_idx = 0
for idx, row in enumerate(df):
    if not row.isnull().all():
        start_row_idx = idx
        break

# Now find the first column where this row is not NaN
start_col = 0
while start_col < len(df.columns) and (df.iloc[start_row_idx][start_col] == '' or pd.isna(df.iloc[start_row_idx][start_col])):
    start_col +=1

if start_col >= len(df.columns):
    return df

# Now find the end column: last column where any row from start_row has non-NaN
end_col = 0
for col in reversed(range(len(df.columns))):
    if any(pd.notna(cell) for cell in df[start_row_idx][col]):
        end_col = col
        break

Then, extract the sub DataFrame from start_row to end_col.

But wait, perhaps even better: after dropping rows with no data and columns with all NaN, find the first non-NaN value's position.

So, using numpy:

import numpy as np

mask = np.isnan(df.values)
if mask.all():
    return df

start_row_idx = np.argmax(~mask.any(axis=1), axis=0).item()
start_col_idx = np.argmax(~mask[start_row_idx, :], axis=None)

end_col_idx = np.max(np.where(~mask[start_row_idx:, :])[1], initial=0)

Then, the DataFrame can be sliced as df[start_row_idx:end_row_idx+1, start_col_idx:end_col_idx_idx+1]

But perhaps this is overcomplicating.

Alternatively, using df.firstnonnaaxis.

Wait, perhaps a better approach:

After reading the data and dropping rows with no data (how='all'):

if df.empty:
    return df

# Find the first column in the first non-empty row that's not NaN
start_col = 0
while start_col < len(df.columns) and (df.iloc[start_row_idx][start_col] is np.nan):
    start_col +=1

if start_col >= len(df.columns):
    return df

end_col = 0
for col in reversed(range(len(df.columns))):
    if any(not pd.isna(cell) for cell in df[start_row_idx:][col]):
        end_col = col
        break

sub_df = df.iloc[start_row_idx:, start_col:end_col+1]

But wait, this might not capture all the columns correctly.

Alternatively, perhaps using df.dropna on columns:

df = df.dropna(axis=1)

if df.empty:
    return df

start_col = 0
while start_col < len(df.columns) and (df.iloc[start_row_idx][start_col] is np.nan):
    start_col +=1

if start_col >= len(df.columns):
    return df

end_col = len(df.columns)-1
for col in range(start_col, len(df.columns)):
    if not df[start_row_idx:][col].all().isna():
        end_col = col
        break

sub_df = df.iloc[start_row_idx:, start_col:end_col+1]

But this might miss some columns beyond the first non-NaN.

Alternatively, perhaps using iloc to find the last column where any cell in the slice from start_row is not NaN.

So:

start_col = 0
while start_col < len(df.columns) and (df.iloc[start_row_idx][start_col] == '' or pd.isna(df.iloc[start_row_idx][start_col])):
    start_col +=1

if start_col >= len(df.columns):
    return df

end_col = len(df.columns)-1
for col in range(start_col, len(df.columns)):
    if not (df.iloc[start_row_idx:][col].all() == '' or pd.isna(df.iloc[start_row_idx:][col])).all():
        end_col = col
        break

sub_df = df.iloc[start_row_idx:, start_col:end_col+1]

But I'm not sure.

Alternatively, using numpy to find the first and last non-NaN values in rows and columns.

But perhaps this is overcomplicating.

In any case, the main idea is:

1. Load the data.
2. Drop rows that are entirely NaN.
3. Find the first row with at least one non-NaN value (start_row).
4. From that row, find the first column with a non-NaN value (start_col).
5. Then, extend end_col to include all columns until the last non-NaN in any of those rows.

But I'm not entirely confident about the exact code for this.

Alternatively, perhaps using df.dropna on both axes and then check if the resulting DataFrame is empty.

Wait, perhaps a better approach:

df = pd.read_excel(...)

# Remove NaNs from columns
df = df.dropna(how='all', axis=1)
if df.empty:
    return df

start_row_idx = 0
for i in range(len(df)):
    if not df.iloc[i].isnull().all():
        start_row_idx = i
        break

# Now find the first column with non-null in that row
start_col = 0
while start_col < len(df.columns) and (df.iloc[start_row_idx][start_col] == '' or pd.isna(df.iloc[start_row_idx][start_col])):
    start_col +=1

if start_col >= len(df.columns):
    return df

# Now find the last column in that row where non-null is present
end_col = 0
for col in reversed(range(len(df.columns))):
    if not (df.iloc[start_row_idx][col] == '' or pd.isna(df.iloc[start_row_idx][col])):
        end_col = col
        break

# Now, check all rows from start_row to see how many columns are non-null
all_cols_non_null = [col for col in range(start_col, len(df.columns)) if any(not (pd.isna(x) or x == '') for x in df[i][start_col:end_col+1] for i in range(start_row_idx,
len(df))]

if not all_cols_non_null:
    return df

end_col = max(all_cols_non_null)

# Extract the sub DataFrame
sub_df = df.iloc[start_row_idx:, start_col:end_col+1]

But this seems complex.

Alternatively, perhaps using df.stack and then unwrapping, but I'm not sure.

Wait, maybe the initial approach is sufficient.

So, to summarize:

- Load the data into a DataFrame.
- Remove any rows that are completely NaN (using dropna with how='all').
- If the resulting DataFrame is empty, return it.
- Find the first non-NaN row (start_row).
- From that row, find the first and last columns that have non-NaN values.
- Extract this sub-DataFrame.

But in code:

df = pd.read_excel('somefile.xlsx')
df = df.dropna(how='all')

if not df.empty:
    start_row_idx = 0
    for i in range(len(df)):
        if not df.iloc[i].isnull().all():
            start_row_idx = i
            break

    # Find first column with non-null in start_row
    start_col = 0
    while start_col < len(df.columns) and (df.iloc[start_row_idx][start_col] == '' or pd.isna(df.iloc[start_row_idx][start_col])):
        start_col +=1
    if start_col >= len(df.columns):
        return df

    # Find last column with non-null in any row from start_row onwards
    end_col = 0
    for col in reversed(range(len(df.columns))):
        # Check if any cell from start_row to end is not NaN
        has_non_null = False
        for i in range(start_row_idx, len(df)):
            if not (pd.isna(df.iloc[i][col]) or df.iloc[i][col] == ''):
                has_non_null = True
                break
        if has_non_null:
            end_col = col
            break

    # Extract the sub-DataFrame
    sub_df = df[start_row_idx:].iloc[:, start_col:end_col+1]
else:
    return df

But wait, in the code above, when finding end_col, I check all columns from start_col to len(df.columns)-1, and for each column, I look through all rows from start_row
onwards. If any of those cells is not NaN or empty, then this column has non-null values, so we set end_col to this column.

But that might include more columns than necessary because even if a column in an earlier row has nulls but has non-null in later rows, it's still included as end_col.

Wait, no. Because after start_row_idx, all the rows from start_row onwards have at least one non-null (since we found start_row as the first row with any non-null). So when
checking a column, if any of those rows (from start_row to end) has a non-null in that column, then this column is considered as part of the data.

Thus, the code should correctly find the right end_col.

But let me test this logic with an example.

Suppose we have columns A, B, C, D.

Rows:

Row 0: NaN NaN NaN NaN
Row 1: NaN NaN 5 NaN
Row 2: NaN NaN NaN 10
Row 3: NaN 20 NaN NaN

After dropna how='all', rows 0 are removed because all are NaN. Wait no, in the initial step, we remove only entire rows that are entirely NaN. So if a row has any non-NaN
values, it remains.

Wait no, dropna with how='all' removes rows where all elements are NaN. So in this case:

Rows after dropna would be rows 1,2,3 because each has at least one non-NaN.

Start_row is 0 (if row 0 is kept) or 1 if row 0 was removed.

Wait no, original df after dropna how='all' would include only rows that have at least one non-NaN. So in the example above, row 0: all NaN → removed; rows 1-3: retained.

Thus, start_row_idx is 0 (since it's the first row after dropping).

Wait no, index starts from 0. If original df has rows 0 to 3:

Row 0: all NaN → dropna removes it.

So the remaining rows are 1,2,3, with indices now 0,1,2 in the new DataFrame.

Wait no, when you call dropna on a DataFrame, it doesn't change the index. So if row 0 is removed, the next row becomes row 0, etc.

But in any case, after dropna how='all', only rows with at least one non-NaN are kept.

So for our example:

After dropna, rows are 1,2,3 (now indices 0,1,2).

Now, start_row_idx is 0 (first row after dropna), which is the original row 1.

From that row, we check each column until a non-NaN is found.

In this case, for row 0 (original row 1):

Column A: NaN → move on.
Column B: NaN → move on.
Column C: 5 → stop. So start_col is 2.

Then, end_col:

Check columns from D to A (since reversed). Wait no, len(df.columns) is 4, so in the loop it's range(3, -1, -1)? Wait, code uses for col in reversed(range(len(df.columns))),
which would be 3,2,1,0.

For each column:

Col 3: Check row 0 (original row 1): NaN. Next, check row 1: 10 → non-NaN → has_non_null is^C