How to Troubleshoot Pandas DataFrame Shape Issues

When working with Pandas, it’s common to encounter situations where the expected output doesn’t match what you anticipate. One such scenario is when using `df.shape` to retrieve the dimensions of a DataFrame. If you find yourself in a situation where `df.shape` isn’t providing any output, it can be frustrating. Let’s explore some possible reasons for this issue and how to troubleshoot it.

Table of Contents

1. Check Data Loading.

The first step in troubleshooting is to ensure that your data is loaded correctly into the DataFrame.

In the provided code snippet, data is loaded from Excel files into DataFrames `df1` and `df2`. It’s crucial to verify that the data is loaded without any errors.
```
df1 = pd.read_excel("Downloads/file1.xlsx", index_col=None)
df2 = pd.read_excel("Downloads/file2.xlsx", index_col=None)
```

2. Verify DataFrame Contents.

After loading the data, it’s essential to confirm that the DataFrames contain the expected data.

You can do this by printing the first few rows using the `head()` method.

print("---file1---")
print(df1.head(3))

print("---file2---")
print(df2.head(3))

If the data is not as expected, it could indicate issues with data loading or formatting.

3. Check DataFrame Shape.

Next, verify the shape of the DataFrames using the `shape` attribute.

print("---file1---")
print(df1.shape)

print("---file2---")
print(df2.shape)

If `df.shape` is not providing any output for `df1` but works as expected for `df2`, it suggests that there might be specific issues with `df1` causing this behavior.

4. Investigate Data Differences.

Since both DataFrames have the same columns but potentially different row counts and data, it’s crucial to investigate any differences between them.

This could include discrepancies in column names, data types, or missing values.

# Check for any differences in column names
print("Columns in df1:", df1.columns)
print("Columns in df2:", df2.columns)

# Check for differences in row counts
print("Row count in df1:", len(df1))
print("Row count in df2:", len(df2))

# Further analysis to identify any discrepancies in data
# such as missing values or unexpected data types

5. Ensure Consistency in Data Formatting.

Inconsistent data formatting, especially when reading from Excel files, can lead to unexpected behavior.

Ensure that the data in both Excel files is formatted consistently and does not contain any hidden characters or formatting issues.

6. Full Example.

6.1 Example Datasets.

For demonstration purposes, let’s create example datasets resembling the structure of the DataFrames loaded from Excel files.

Example DataFrame 1 (df1):

|  ID  | Name  | Age | Gender | City    | Income | Education |
|------|-------|-----|--------|---------|--------|-----------|
| 1001 | Alice | 25  | Female | New York| 50000  | Graduate  |
| 1002 | Bob   | 30  | Male   | Chicago | 60000  | Graduate  |
| 1003 | Cindy | 28  | Female | Boston  | 55000  | Undergrad |
| 1004 | David | 35  | Male   | Houston | 70000  | Graduate  |
| 1005 | Emily | 32  | Female | Atlanta | 65000  | Graduate  |

Example DataFrame 2 (df2):

|  ID  | Name  | Age | Gender | City    | Income | Education |
|------|-------|-----|--------|---------|--------|-----------|
| 2001 | Frank | 40  | Male   | Seattle | 75000  | Graduate  |
| 2002 | Grace | 27  | Female | Denver  | 58000  | Graduate  |
| 2003 | Henry | 33  | Male   | Miami   | 68000  | Graduate  |
| 2004 | Irene | 29  | Female | Phoenix | 60000  | Undergrad |
| 2005 | Jack  | 31  | Male   | Dallas  | 67000  | Graduate  |

You can save the above example dataset into a text file, and then load them into a Microsoft Excel file.
In this example, we import the above data to 2 Excel files example_dataset_file1.xlsx and example_dataset_file2.xlsx.

6.2 Example Source Code.

Below is the full example source code.

import pandas as pd

# Create DataFrame df1 and df2
df1 = pd.read_excel("./resource-files/excel-example-data-files/example_dataset_file1.xlsx", index_col=None)
df2 = pd.read_excel("./resource-files/excel-example-data-files/example_dataset_file2.xlsx", index_col=None)

# Print the first few rows of df1 and df2
print("---file1---")
print(df1.head(3))

print("---file2---")
print(df2.head(3))

# Print the shape of df1 and df2
print("---file1---")
print(df1.shape)

print("---file2---")
print(df2.shape)

# Check for any differences in column names
print("Columns in df1:", df1.columns)
print("Columns in df2:", df2.columns)

# Check for differences in row counts
print("Row count in df1:", len(df1))
print("Row count in df2:", len(df2))

The 2 Excel files are saved in the folder

./resource-files/excel-example-data-files/.

When you run the above Python source code, it will generate the below output.

---file1---
     ID     Name     Age    Gender    City       Income    Education 
0    1001   Alice      25   Female    New York     50000   Graduate  
1    1002   Bob        30   Male      Chicago      60000   Graduate  
2    1003   Cindy      28   Female    Boston       55000   Undergrad 
---file2---
     ID     Name     Age    Gender    City       Income    Education 
0    2001   Frank      40   Male      Seattle      75000   Graduate  
1    2002   Grace      27   Female    Denver       58000   Graduate  
2    2003   Henry      33   Male      Miami        68000   Graduate  
---file1---
(5, 7)
---file2---
(5, 7)
Columns in df1: Index(['  ID  ', ' Name  ', ' Age ', ' Gender ', ' City    ', ' Income ',
       ' Education '],
      dtype='object')
Columns in df2: Index(['  ID  ', ' Name  ', ' Age ', ' Gender ', ' City    ', ' Income ',
       ' Education '],
      dtype='object')
Row count in df1: 5
Row count in df2: 5

7. Conclusion.

Troubleshooting issues like `df.shape` not providing any output requires a systematic approach.
By verifying data loading, checking DataFrame contents, investigating data differences, and ensuring consistency in data formatting, you can effectively diagnose and resolve such issues.
Remember to pay attention to details and use Python’s debugging tools to pinpoint the root cause of the problem.