I'am searching a mthode of multiple correspondence analysis using python,
To do a multiple correspondence analysis (MCA) with Python, you can use the mca package. This package is not included in the standard library,
Therefore, you will need to install it first using pip install mca
for further details visit : https://pypi.org/project/mca/
Multiple correspondence analysis (MCA) is a statistical method for exploring and visualizing relationships between categorical variables. It is commonly used in the social sciences to analyze survey data.
To perform MCA with Python, you can use the prince library. Here is an example of how you can use this library to perform MCA on a Pandas DataFrame:
##########################3
import prince
# Load the data into a Pandas DataFrame
df = pd.read_csv('data.csv')
# Create the MCA object
mca = prince.MCA(n_components=2)
# Fit the MCA model to the data
mca.fit(df)
# Print the eigenvalues
print(mca.eigenvalues_)
# Plot the MCA results
mca.plot_coordinates(df, ax=None, figsize=(6, 6), show_row_points=True, row_points_size=10, show_row_labels=False, show_column_points=True, column_points_size=30, show_column_labels=False, legend_n_cols=1)
#################################
In this example, df is the Pandas DataFrame that contains the data for the analysis. The n_components parameter specifies the number of dimensions to keep in the MCA model. The fit() method fits the MCA model to the data, and the eigenvalues_ attribute returns the eigenvalues of the model.
The plot_coordinates() method is used to visualize the MCA results. This function creates a scatter plot of the MCA components, with each row and column of the data represented by a point. The show_row_points, show_row_labels, show_column_points, and show_column_labels parameters control whether the points and labels for the rows and columns are displayed.
I hope this helps!
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