Open ozkary opened 1 year ago
test api via postman
Generated code:
import pandas as pd
def transform_data(csv_path):
'''
Transform a data frame by consolidating the 'date' and 'time' columns into a date time field column named 'created'.
Parameters
----------
csv_path : str
The path of the CSV file to be transformed
Returns
Code:
import pandas as pd import unittest
def transform_data(csv_file_path): """ Transform a data frame by consolidating the 'date' and 'time' columns into a date time field column named 'created'.
Parameters:
csv_file_path (str): The path of the CSV file.
Returns:
bool: True if successful, False if not.
"""
try:
# Load the data from the CSV file
data_frame = pd.read_csv(csv_file_path)
# Combine the 'date' and 'time' columns into a single 'created' column
data_frame['created'] = pd.to_datetime(data_frame['date'] + ' ' + data_frame['time'])
# Save the resulting data frame to disk in Parquet format
data_frame.to_parquet('data_frame.parquet')
Generated code:
import pandas as pd
import unittest
def transform_data(csv_file_path):
"""
Transform a data frame by consolidating the 'date' and 'time' columns into a date time field column named 'created'.
Parameters:
csv_file_path (str): The path of the CSV file to be transformed.
Returns:
bool: True if the transformation was successful, False otherwise.
"""
# Load the data from the CSV file
df = pd.read_csv(csv_file_path)
# Convert the 'date' and 'time' columns into a single 'created' column
df['created'] = pd.to_datetime(df['date'] + ' ' + df['time'])
# Save the transformed data frame to disk in Parquet format
df.to_parquet('transformed_data.parquet')
return True
class TransformDataTest(unittest.TestCase):
"""Unit tests for the `transform_data` function."""
def test_with_valid_data(self):
"""Test the function with valid data."""
# Create a mock CSV file
with open('mock_data.csv', 'w') as f:
f.write('date,time\n')
Generated code:
import pandas as pd
import unittest
def transform_data(csv_file_path):
"""
Transforms a data frame by consolidating the 'date' and 'time' columns into a date time field column named 'created'.
Parameters:
csv_file_path (str): The path to the CSV file to transform.
Returns:
bool: True if successful, False if not.
"""
try:
# Load the CSV file into a pandas data frame
df = pd.read_csv(csv_file_path)
# Create a new column 'created' by combining the 'date' and 'time' columns
df['created'] = pd.to_datetime(df['date'] + ' ' + df['time'])
# Drop the 'date' and 'time' columns
df.drop(['date', 'time'], axis=1, inplace=True)
# Save the resulting data frame to disk in Parquet format
df.to_parquet('transformed_data.parquet')
return True
except:
return False
class TestTransformData(unittest.TestCase):
"""Unit tests for the `transform_data` function."""
def test_transform_data_success(self):
Generated code:
# Import Libraries
import pandas as pd
import pytest
# Transform Data Function
def transform_data(csv_file_path):
'''
Transforms a data frame by consolidating the 'date' and 'time' columns into a date time field column named 'created'.
Parameters:
csv_file_path (str): The path to the CSV file containing the data to transform.
Returns:
success (bool): True if successful, false if not.
'''
try:
# Load data from CSV file
df = pd.read_csv(csv_file_path)
# Consolidate date and time columns into a date time field
df['created'] = pd.to_datetime(df['date'] + ' ' + df['time'])
# Save data frame to disk in Parquet format
df.to_parquet('transformed_data.parquet')
# Return success
return True
except:
# Return failure
return False
# Unit Tests
@pytest.mark.parametrize('csv_file_path, expected_result', [
('test_data.csv', True),
('invalid_path.csv', False)
])
def test_transform_data(csv_
Generated code:
## transform_data Function
This function takes a file path to a CSV file and transforms it by consolidating the 'date' and 'time' columns into a `created` column of type `datetime`.
```python
import pandas as pd
def transform_data(csv_file_path):
# Read CSV file
df = pd.read_csv
$Provider.GEMINI Generated code:
import pandas as pd
def transform_data(csv_path, output_path):
"""
Transforms a data frame by consolidating the 'date' and 'time' columns into a date time field column named 'created'.
Args:
csv_path (str): The path to the CSV file containing the data.
output_path (str): The path to the Parquet file where the transformed data should be saved.
Returns:
bool: True if the transformation was successful, False otherwise.
"""
try:
# Load the data from the CSV file
df = pd.read_csv(csv_path)
# Consolidate the 'date' and 'time' columns into a date time field column named 'created'
df['created'] = pd.to_datetime(df['date'] + ' ' + df['time'])
# Drop the 'date' and 'time' columns
df.drop(columns=['date', 'time'], inplace=True)
# Save the resulting data frame to disk in Parquet format
df.to_parquet(output_path)
return True
except Exception as e:
print(f'Error transforming data: {e}')
return False
def test_transform_data():
"""
Unit test for the `transform_data` function.
"""
# Create a test data frame
df = pd.DataFrame({
'date': ['2023-03-08', '2023-03-09', '2023-03-10'],
'time': ['10:00:00', '11:00:00', '12:00:00'],
'value': [1, 2, 3]
})
# Transform the test data frame
transformed_df = transform_data(df)
# Check that the transformed data frame has the expected shape and columns
assert transformed_df.shape == (3, 2)
assert 'created' in transformed_df.columns
# Check that the 'created' column has the expected values
assert transformed_df['created'][0] == pd.to_datetime('2023-03-08 10:00:00')
assert transformed_df['created'][1] == pd.to_datetime('2023-03-09 11:00:00')
assert transformed_df['created'][2] == pd.to_datetime('2023-03-10 12:00:00')
if __name__ == '__main__':
# Test the `transform_data` function
test_transform_data()
Provider.GEMINI Generated code:
import pandas as pd
def transform_data(csv_file_path, output_file_path):
"""
Transforms a data frame by consolidating the 'date' and 'time' columns into a date time field column named 'created'.
Args:
csv_file_path: The CSV file path of the data frame to transform.
output_file_path: The Parquet file path to save the resulting data frame.
Returns:
True if successful, False otherwise.
"""
try:
# Load the data frame from the CSV file
df = pd.read_csv(csv_file_path)
# Consolidate the 'date' and 'time' columns into a date time field column
df['created'] = pd.to_datetime(df['date'] + ' ' + df['time'])
# Drop the original 'date' and 'time' columns
df.drop(columns=['date', 'time'], inplace=True)
# Save the resulting data frame to disk in Parquet format
df.to_parquet(output_file_path)
return True
except Exception as e:
print('Error transforming data frame:', e)
return False
def test_transform_data():
# Create a test data frame
test_df = pd.DataFrame({
'date': ['2023-03-08', '2023-03-09', '2023-03-10'],
'time': ['10:00:00', '11:00:00', '12:00:00'],
'value': [1, 2, 3]
})
# Transform the test data frame
transformed_df = transform_data(test_df, 'test_output.parquet')
# Assert that the transformed data frame has the correct shape and columns
assert transformed_df.shape == (3, 2)
assert list(transformed_df.columns) == ['created', 'value']
# Assert that the 'created' column is of type datetime
assert transformed_df['created'].dtype == 'datetime64[ns]'
# Assert that the 'created' column contains the correct values
assert transformed_df['created'].tolist() == ['2023-03-08 10:00:00', '2023-03-09 11:00:00', '2023-03-10 12:00:00']
if __name__ == '__main__':
test_transform_data()
As a data scientist, I want to generate code using the following technologies, requirements, and specifications:
Technologies:
Requirements:
Specifications:
transform_data
function to verify its correctness. The unit test should cover different scenarios and assert the expected behavior of the function.