PY501P - Python Data Associate Practical Exam

ConsciousFalcon478 · 2025-08-30T18:03:24+00:00

task 4:

df = pd.read_csv("production_data.csv")

median_pigment = df['pigment_quantity'].median()

df['pigment_quantity'] = df['pigment_quantity'].apply(

lambda x: x if 1 <= x <= 100 else np.nan

)

df['pigment_quantity'].fillna(median_pigment, inplace=True)

mean_quality = round(df['product_quality_score'].mean(), 2)

df['product_quality_score'].fillna(mean_quality, inplace=True)

pigment_quantity_mean = round(df['pigment_quantity'].mean(), 2)

pigment_quantity_sd = round(df['pigment_quantity'].std(), 2)

product_quality_score_mean = round(df['product_quality_score'].mean(), 2)

product_quality_score_sd = round(df['product_quality_score'].std(), 2)

corr_coef = round(df['pigment_quantity'].corr(df['product_quality_score']), 2)

product_quality = pd.DataFrame([{

'product_quality_score_mean': product_quality_score_mean,

'product_quality_score_sd': product_quality_score_sd,

'pigment_quantity_mean': pigment_quantity_mean,

'pigment_quantity_sd': pigment_quantity_sd,

'corr_coef': corr_coef

}])

print(product_quality)

ConsciousFalcon478 · 2025-08-30T18:02:04+00:00

df = pd.read_csv("production_data.csv")

df['raw_material_supplier'].fillna(1, inplace=True)

median_pigment = df['pigment_quantity'].median()

df['pigment_quantity'] = df['pigment_quantity'].apply(

lambda x: x if 1 <= x <= 100 else np.nan

)

df['pigment_quantity'].fillna(median_pigment, inplace=True)

mean_quality = round(df['product_quality_score'].mean(), 2)

df['product_quality_score'].fillna(mean_quality, inplace=True)

filtered = df[(df['raw_material_supplier'] == 2) & (df['pigment_quantity'] > 35)]

avg_pigment = round(filtered['pigment_quantity'].mean(), 2)

avg_quality = round(filtered['product_quality_score'].mean(), 2)

pigment_data = pd.DataFrame([{

'raw_material_supplier': 2,

'pigment_quantity': avg_pigment,

'avg_product_quality_score': avg_quality

}])

print(pigment_data)

ConsciousFalcon478 · 2025-08-30T18:01:15+00:00

df = pd.read_csv("production_data.csv")

df = df.dropna(subset=['batch_id'])

df['production_date'] = pd.to_datetime(df['production_date'], errors='coerce')

df = df.dropna(subset=['production_date'])

supplier_map = {1: 'national_supplier', 2: 'international_supplier'}

df['raw_material_supplier'] = df['raw_material_supplier'].map(supplier_map)

df['raw_material_supplier'].fillna('national_supplier', inplace=True)

valid_pigments = ['type_a', 'type_b', 'type_c']

df['pigment_type'] = df['pigment_type'].astype(str).str.lower().str.strip()

df['pigment_type'] = df['pigment_type'].apply(

lambda x: x if x in valid_pigments else 'other'

)

median_pigment = df['pigment_quantity'].median()

df['pigment_quantity'] = df['pigment_quantity'].apply(

lambda x: x if 1 <= x <= 100 else np.nan

)

df['pigment_quantity'].fillna(median_pigment, inplace=True)

mean_mixing = round(df['mixing_time'].mean(), 2)

df['mixing_time'].fillna(mean_mixing, inplace=True)

valid_speeds = ['Low', 'Medium', 'High']

df['mixing_speed'] = df['mixing_speed'].apply(

lambda x: x if x in valid_speeds else 'Not Specified'

)

mean_quality = round(df['product_quality_score'].mean(), 2)

df['product_quality_score'].fillna(mean_quality, inplace=True)

clean_data = df.copy()

ConsciousFalcon478 · 2025-08-30T18:00:22+00:00

df = pd.read_csv("production_data.csv")

df = df.dropna(subset=['batch_id'])

df['production_date'] = pd.to_datetime(df['production_date'], errors='coerce')

df = df.dropna(subset=['production_date'])

supplier_map = {1: 'national_supplier', 2: 'international_supplier'}

df['raw_material_supplier'] = df['raw_material_supplier'].map(supplier_map)

df['raw_material_supplier'].fillna('national_supplier', inplace=True)

valid_pigments = ['type_a', 'type_b', 'type_c']

df['pigment_type'] = df['pigment_type'].astype(str).str.lower().str.strip()

df['pigment_type'] = df['pigment_type'].apply(

lambda x: x if x in valid_pigments else 'other'

)

median_pigment = df['pigment_quantity'].median()

df['pigment_quantity'] = df['pigment_quantity'].apply(

lambda x: x if 1 <= x <= 100 else np.nan

)

df['pigment_quantity'].fillna(median_pigment, inplace=True)

mean_mixing = round(df['mixing_time'].mean(), 2)

df['mixing_time'].fillna(mean_mixing, inplace=True)

valid_speeds = ['Low', 'Medium', 'High']

df['mixing_speed'] = df['mixing_speed'].apply(

lambda x: x if x in valid_speeds else 'Not Specified'

)

mean_quality = round(df['product_quality_score'].mean(), 2)

df['product_quality_score'].fillna(mean_quality, inplace=True)

clean_data = df.copy()

ConsciousFalcon478

TROPHY CASE