# pandascompare
<div>
[](https://pypi.org/project/pandascompare/)
[](https://github.com/zteinck/pandascompare/blob/master/LICENSE)
</div>
`pandascompare` is a Python package designed to compare `DataFrame` objects, enabling you to quickly identify the differences between two datasets.
## Installation
```sh
pip install pandascompare
```
## Main Features
The `PandasCompare` class compares any two `DataFrame` objects along the following dimensions:
- `Rows` ➔ discrepancies with respect to the join key(s) specified via the `join_on` argument.
- `Columns` ➔ name differences or missing columns.
- `Values` ➔ data that differs in terms of value or type.
## Example Usage
Please refer to the documentation within the code for more information.
### Imports
```python
from pandascompare import PandasCompare
```
### Create DataFrames
First, let's create two sample `DataFrame` objects to compare.
```python
import pandas as pd
import numpy as np
# February Data
left_df = pd.DataFrame({
'id': [1, 2, 3],
'date': [pd.to_datetime('2024-02-29')] * 3,
'first_name': ['Alice', 'Mike', 'John'],
'amount': [10.5, 5.3, 33.77],
})
# January Data
right_df = pd.DataFrame({
'id': [1, 2, 9],
'date': [pd.to_datetime('2024-01-31')] * 3,
'first_name': ['Alice', 'Michael', 'Zachary'],
'last_name': ['Jones', 'Smith', 'Einck'],
'amount': [11.1, np.nan, 14],
})
```
### Compare DataFrames
Next, we will initialize a `PandasCompare` instance to perform the comparison. Please consult the in-code documentation for a comprehensive list of available arguments.
```python
pc = PandasCompare(
left=left_df,
right=right_df,
left_label='feb',
right_label='jan',
join_on='id',
left_ref=['first_name'],
include_delta=True,
verbose=True,
)
```
### Export to Excel
Finally, let's export the compare report to an Excel file to view the results.
```python
pc.export_to_excel()
```
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"description": "# pandascompare\n\n<div>\n\n[](https://pypi.org/project/pandascompare/)\n[](https://github.com/zteinck/pandascompare/blob/master/LICENSE)\n\n</div>\n\n`pandascompare` is a Python package designed to compare `DataFrame` objects, enabling you to quickly identify the differences between two datasets. \n\n## Installation\n```sh\npip install pandascompare\n```\n\n## Main Features\nThe `PandasCompare` class compares any two `DataFrame` objects along the following dimensions:\n- `Rows` \u2794 discrepancies with respect to the join key(s) specified via the `join_on` argument.\n- `Columns` \u2794 name differences or missing columns.\n- `Values` \u2794 data that differs in terms of value or type.\n\n\n## Example Usage\nPlease refer to the documentation within the code for more information.\n\n### Imports\n```python\nfrom pandascompare import PandasCompare\n```\n\n### Create DataFrames\nFirst, let's create two sample `DataFrame` objects to compare.\n```python\nimport pandas as pd\nimport numpy as np\n\n# February Data\nleft_df = pd.DataFrame({\n 'id': [1, 2, 3],\n 'date': [pd.to_datetime('2024-02-29')] * 3,\n 'first_name': ['Alice', 'Mike', 'John'],\n 'amount': [10.5, 5.3, 33.77],\n })\n\n# January Data\nright_df = pd.DataFrame({\n 'id': [1, 2, 9],\n 'date': [pd.to_datetime('2024-01-31')] * 3,\n 'first_name': ['Alice', 'Michael', 'Zachary'],\n 'last_name': ['Jones', 'Smith', 'Einck'],\n 'amount': [11.1, np.nan, 14],\n })\n```\n\n### Compare DataFrames\nNext, we will initialize a `PandasCompare` instance to perform the comparison. Please consult the in-code documentation for a comprehensive list of available arguments.\n```python\npc = PandasCompare(\n left=left_df,\n right=right_df,\n left_label='feb',\n right_label='jan',\n join_on='id',\n left_ref=['first_name'],\n include_delta=True,\n verbose=True,\n )\n```\n\n### Export to Excel\nFinally, let's export the compare report to an Excel file to view the results.\n```python\npc.export_to_excel()\n```\n",
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