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# Function to find outliers using IQR
def find_outliers_IQR(df):
    outlier_indices = []
    df = df.select_dtypes(include=['number'])
    for column in df.columns:
        Q1 = df[column].quantile(0.25)
        Q3 = df[column].quantile(0.75)
        IQR = Q3 - Q1
        lower_bound = Q1 - 1.5 * IQR
        upper_bound = Q3 + 1.5 * IQR

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# define the expected types
expected_types = {'recipe': 'int64', 
                  'calories': 'float64', 
                  'carbohydrate': 'float64',
                  'sugar': 'float64', 
                  'protein': 'float64',
                  'category': 'str', 
                  'servings': 'int64',
                  'high_traffic': 'bool'               
                  }

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def check_missing_data(df):
    # Check for missing values
    proportion_null_rows = 100*(round(df.isnull().any(axis=1).sum()/df.any(axis=1).count(),2))
    if proportion_null_rows <= 5:
        print(f"There are {df.isnull().any(axis=1).sum()} rows with a null value. All of them are erased!")
        df.dropna()
    else:
        print("Too many null values, we need to check columns by columns further.")
        if df.isnull().sum().sum() > 0:
            print("\nProportion of missing values by column")

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# Check if there are duplicates
def drop_duplicates(df, columns=None): 
	if columns == None: 
		df.drop_duplicates(inplace=True) 
	else: 
		df.drop_duplicates(subset = columns, inplace=False)
	return df 

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# Function to read data based on file extension
def read_data(file_path):
    _ , file_ext = os.path.splitext(file_path)
    if file_ext == '.csv':
        return pd.read_csv(file_path)
    elif file_ext == '.json':
        return pd.read_json(file_path)
    elif file_ext in ['.xls', '.xlsx']:
        return pd.read_excel(file_path)
    else:

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import seaborn as sns
import matplotlib.pyplot as plt

# Select only numeric columns for correlation
numeric_df = df.select_dtypes(include=['float64', 'int64'])

plt.figure(figsize=(8, 4))

import seaborn as sns
sns.heatmap(numeric_df.corr(), annot=True, fmt=".0%")

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#let Create a pair plot of some columns 
df_temp = df.drop(columns=["bill_length_mm", "body_mass_g"])
sns.pairplot(df.iloc[:,:],hue='species')  # graph also  tell us about the the realationship between the two columns 

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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression

# Prepare the data
df_dummy = pd.get_dummies(df, dtype=int).sort_values(by='flipper_length_mm', ascending=True)

# Select the features and the target
X = df_dummy.iloc[:, 1:3]  # Assuming that the features are in the 3rd and 4th columns

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import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

# One-hot encode the categorical data and sort by flipper_length_mm
df_dummy = pd.get_dummies(df, dtype=int).sort_values(by='flipper_length_mm', ascending=True)

# Select the features and the target variable

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from sklearn.linear_model import LinearRegression
import random

# create linear regression object
lr = LinearRegression()

# fit linear regression
lr.fit(df_multiple[['Height','Gender']], df['Weight'])

x_values = np.linspace(55,80,100)