PROBLEM.md

🦖 Question

i can filter columns,

data_set_06 = data_set_05[["Gender","Married"]]
data_set_06

and then use

pd.get_dummies(data_set_06)

.   .   .

so how

obj = OneHotEncoder()
obj.fit_transform(encoded_dataset)

it can give output -

<Compressed Sparse Row sparse matrix of dtype 'float64'
   with 2456 stored elements and shape (614, 8)

Because of i already filter two columns => Which break in 2 ,
But, how it access 4 columns => Which break into 8 ?

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🦖 Reply

I understand your question now. Let's clarify how OneHotEncoder works in relation to the columns you've filtered and encoded. Here’s the step-by-step explanation:

Step-by-Step Explanation:

1. Data Preparation:
   • First, you create a copy of your original dataset (dataset) and handle missing values in the Gender and Married columns by filling them with the mode.

# Create a copy of the original dataset
data_set_04 = dataset.copy()

# Fill missing values with mode in 'Gender' and 'Married' columns
data_set_04["Gender"].fillna(data_set_04["Gender"].mode()[0], inplace=True)
data_set_04["Married"].fillna(data_set_04["Married"].mode()[0], inplace=True)

2. Check Null Counts:
   • After filling missing values, you can check if there are any remaining null values.

# Check null counts in the modified dataset
print(data_set_04.isnull().sum())

3. Filter Columns:
   • Next, you create a copy (data_set_05) of the modified dataset and filter out only the Gender and Married columns into data_set_06.

# Create another copy of the modified dataset
data_set_05 = data_set_04.copy()

# Filter out 'Gender' and 'Married' columns into a new DataFrame
data_set_06 = data_set_05[["Gender", "Married"]]

4. Perform One-Hot Encoding:
   • Use pd.get_dummies() to perform one-hot encoding on data_set_06. This will create binary indicators for each unique category in Gender and Married.

# Perform one-hot encoding on data_set_06
encoded_dataset = pd.get_dummies(data_set_06)

# Display the first few rows to show 1s and 0s
print("First few rows with binary indicators:")
print(encoded_dataset.head())
print()

# Display the summary information of the DataFrame
print("Summary information:")
print(encoded_dataset.info())

5. Initialize and Fit OneHotEncoder:
   • Now, initialize OneHotEncoder (obj) from sklearn.preprocessing and fit it to encoded_dataset. This step adjusts the encoder based on the categories observed in encoded_dataset.

from sklearn.preprocessing import OneHotEncoder

# Initialize OneHotEncoder
obj = OneHotEncoder()

# Fit OneHotEncoder to encoded_dataset
obj.fit(encoded_dataset)

6. Transform and Convert into Array:
   • Once OneHotEncoder is fitted, you can transform encoded_dataset using obj.transform(). This operation converts the categorical data into a sparse matrix representation (<class 'scipy.sparse.csr.csr_matrix'>).

# Transform encoded_dataset into a sparse matrix
array_01 = obj.transform(encoded_dataset)

# Display the shape of the sparse matrix
print("Shape of sparse matrix:", array_01.shape)

7. Convert Sparse Matrix to DataFrame:
   • To create a DataFrame (df) with the correct column names and the transformed data, you use the get_feature_names_out() method to get the column names from obj, then convert array_01 into an array and create df.

# Get feature names for the columns
column_names = obj.get_feature_names_out(encoded_dataset.columns)

# Convert sparse matrix array_01 to a dense array and then to a DataFrame with column_names
df = pd.DataFrame(array_01.toarray(), columns=column_names)

# Display the DataFrame
print(df.head())

Explanation:

• Column Filtering: Even though you initially filter down to only Gender and Married columns (data_set_06), the one-hot encoding (pd.get_dummies()) expands these into multiple binary indicators.

• OneHotEncoder Usage: When you initialize and fit OneHotEncoder (obj), it learns the categories from encoded_dataset, which includes all unique values derived from Gender and Married after one-hot encoding.

• Sparse Matrix: The output of obj.transform() is a sparse matrix (array_01) that efficiently represents the encoded categorical variables, even though you filtered down to fewer original columns.

• DataFrame Creation: By using get_feature_names_out() to obtain column names and converting array_01 into a DataFrame (df), you ensure that df correctly represents the transformed data with the appropriate column names.

This approach ensures that your data preprocessing, filtering, one-hot encoding, and transformation into a format suitable for further analysis or modeling are correctly executed. Adjust variables and methods based on your specific dataset and requirements.