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BigCodeBench / BigCodeBench v0.1.0_hf 13fbe0d5-b6db-55da-a7ca-851c2bfae163
Problem
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complete prompt
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
def task_func(data, n_components=2):
"""
Perform PCA (Principal Component Analysis) on the provided DataFrame.
This function takes a pandas DataFrame, scales the data using sklearn
StandardScaler, and then applies PCA to reduce
the number of dimensions of the data to the number specified by n_components,
maintaining as much information as possible.
Parameters:
data (DataFrame): A pandas DataFrame containing numerical data. Each column represents a
different variable, and each row represents a different observation.
n_components (int): The number of principal components to retain after transformation.
Default is 2.
Returns:
DataFrame: A new DataFrame with the original data transformed into 'n_components' principal
components.
Raises:
ValueError: If input data is not a DataFrame or contains non-numeric data.
ValueError: If n_components is greater than the number of columns in the data.
ValueError: If input data is empty.
Requirements:
pandas
sklearn.preprocessing
sklearn.decomposition
Example:
>>> data = pd.DataFrame({
... 'A': [1, 2, 3, 4, 5],
... 'B': [6, 7, 8, 9, 10],
... 'C': [11, 12, 13, 14, 15],
... 'D': [16, 17, 18, 19, 20]
... })
>>> result = task_func(data, n_components=2)
>>> print(result)
0 1
0 2.828427 3.648565e-16
1 1.414214 -1.216188e-16
2 -0.000000 0.000000e+00
3 -1.414214 1.216188e-16
4 -2.828427 2.432377e-16
>>> data = pd.DataFrame({
... 'A': [-43, 212, 1, -12, 5],
... 'B': [-1, 0, 0, 9.76, 12.34],
... 'C': [1, 42, -13.2, 31, 1.23],
... })
>>> res = task_func(data, n_components=1)
>>> print(res)
0
0 -0.793152
1 2.511947
2 -0.940253
3 0.069179
4 -0.847722
"""
instruct prompt
Perform PCA (Principal Component Analysis) on the provided DataFrame. This function takes a pandas DataFrame, scales the data using sklearn StandardScaler, and then applies PCA to reduce the number of dimensions of the data to the number specified by n_components, maintaining as much information as possible. >>> data = pd.DataFrame({ ... 'A': [-43, 212, 1, -12, 5], ... 'B': [-1, 0, 0, 9.76, 12.34], ... 'C': [1, 42, -13.2, 31, 1.23], ... }) >>> res = task_func(data, n_components=1) >>> print(res) 0 0 -0.793152 1 2.511947 2 -0.940253 3 0.069179 4 -0.847722
The function should raise the exception for: ValueError: If input data is not a DataFrame or contains non-numeric data. ValueError: If n_components is greater than the number of columns in the data. ValueError: If input data is empty.
The function should output with:
DataFrame: A new DataFrame with the original data transformed into 'n_components' principal
components.
You should write self-contained code starting with:
Code
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
def task_func(data, n_components=2):
code prompt
Code
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
def task_func(data, n_components=2):
entry point
task_func
libs
- pandas
- sklearn
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initial import