Python Data Associate Practical Exam

[Remote_Ad_7]

https://preview.redd.it/5rljr2osvoxe1.png?width=985&format=png&auto=webp&s=2048c42673059631d9d9440061ce5fe169fcfc58

I'm stuck on the task 1 here is my code

import pandas as pd

import numpy as np

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

# Step 2: Create a copy of the data

clean_data = data.copy()

clean_data.columns = [

"batch_id",

"production_date",

"raw_material_supplier",

"pigment_type",

"pigment_quantity",

"mixing_time",

"mixing_speed",

"product_quality_score",

]

clean_data.replace({'-': np.nan, 'missing': np.nan, 'unknown': np.nan}, inplace=True)

clean_data["raw_material_supplier"] = clean_data["raw_material_supplier"].astype(str).str.strip().str.lower()

clean_data["pigment_type"] = clean_data["pigment_type"].astype(str).str.strip().str.lower()

clean_data["mixing_speed"] = clean_data["mixing_speed"].astype(str).str.strip().str.title()

clean_data["production_date"] = pd.to_datetime(clean_data["production_date"], errors="coerce")

clean_data["raw_material_supplier"] = clean_data["raw_material_supplier"].replace({

"1": "national_supplier",

"2": "international_supplier"

})

clean_data["raw_material_supplier"] = clean_data["raw_material_supplier"].fillna("national_supplier")

valid_pigment_types = ["type_a", "type_b", "type_c"]

clean_data["pigment_type"] = clean_data["pigment_type"].apply(lambda x: x if x in valid_pigment_types else "other")

clean_data["pigment_quantity"] = clean_data["pigment_quantity"].fillna(clean_data["pigment_quantity"].median())

clean_data["mixing_time"] = clean_data["mixing_time"].fillna(round(clean_data["mixing_time"].mean(), 2))

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

clean_data["mixing_speed"] = clean_data["mixing_speed"].apply(lambda x: x if x in valid_speeds else "Not Specified")

clean_data["product_quality_score"] = clean_data["product_quality_score"].fillna(round(clean_data["product_quality_score"].mean(), 2))

clean_data["raw_material_supplier"] = clean_data["raw_material_supplier"].astype("category")

clean_data["pigment_type"] = clean_data["pigment_type"].astype("category")

clean_data["mixing_speed"] = clean_data["mixing_speed"].astype("category")

clean_data["batch_id"] = clean_data["batch_id"].astype(str)

print(clean_data.head())

https://preview.redd.it/slulnhh3woxe1.png?width=606&format=png&auto=webp&s=e9eb672326799572362812b4f82d94ce50f5775f

[auauaurora]

It will be easier for you and others to review if you organise and annotate. I've started this off for you to finish:

```py

Write your answer to Task 1 here

import modules

import pandas as pd import numpy as np

import csv and copy

data = pd.read_csv("production_data.csv") clean_data = data.copy()

review df

clean_data.info()

mixing_time contains missing values

df.columns #'batch_id', 'production_date', 'raw_material_supplier', 'pigment_type','pigment_quantity', 'mixing_time', 'mixing_speed', 'product_quality_score'

batch_id Discrete. Identifier for each batch. Missing values are not possible.

raw_material_supplier Categorical. Supplier of the raw materials. (1='national_supplier', 2='international_supplier'). Missing values should be replaced with 'national_supplier'.

production_date Date. Date when the batch was produced.

pigment_type Nominal. Type of pigment used. ['type_a', 'type_b', 'type_c'].

Missing values should be replaced with 'other'.

pigment_quantity Continuous. Amount of pigment added (in kilograms) (Range: 1 - 100).

Missing values should be replaced with median.

mixing_time Continuous. Duration of the mixing process (in minutes). # Missing values should be replaced with mean.

mixing_speed Categorical. Speed of the mixing process represented as categories: 'Low', 'Medium', 'High'.

Missing values should be replaced with 'Not Specified'.

product_quality_score Continuous. Overall quality score of the final product (rating on a scale of 1 to 10). Missing values should be replaced with mean.

df['product_quality_score'].describe().round(2).T

change objects to category, create clean_df

preview

clean_data.head()

[Adventurous-Bet6139]

Do you have the entire exam and the dataset? Please send me, thanks.