How to use ChatGPT for Data Scientist Job Role

python

   import pandas as pd
   df = pd.read_csv('your_file.csv')
   

python

   print(df.head())
   print(df.info())
   

python

   df.isnull().sum()  # Check for missing values
   df.fillna(method='ffill', inplace=True)  # Forward fill missing values
   

python

   df.drop_duplicates(inplace=True)
   

python

   df['date_column'] = pd.to_datetime(df['date_column'])  # Convert to datetime
   df['category_column'] = df['category_column'].astype('category')  # Convert to category
   

python

   df['text_column'] = df['text_column'].str.lower()  # Convert to lowercase
   

python

   df = df[(df['numeric_column'] >= lower_bound) & (df['numeric_column'] <= upper_bound)]  # Filter out outliers
   

python

   import pandas as pd
   from sklearn.model_selection import train_test_split
   from sklearn.ensemble import RandomForestClassifier
   from sklearn.metrics import accuracy_score, classification_report
   
   df = pd.read_csv('your_file.csv')
   

python

   X = df.drop('target_column', axis=1)  # Replace 'target_column' with your actual target column name
   y = df['target_column']
   
   # If you have categorical variables, convert them to dummy variables
   X = pd.get_dummies(X, drop_first=True)
   

python

   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
   

python

   model = RandomForestClassifier(n_estimators=100, random_state=42)
   model.fit(X_train, y_train)
   

python

   y_pred = model.predict(X_test)
   

python

   accuracy = accuracy_score(y_test, y_pred)
   print(f'Accuracy: {accuracy:.2f}')
   print(classification_report(y_test, y_pred))
   

python

   from scipy import stats
   t_stat, p_value = stats.ttest_ind(group1, group2)  # For independent t-test
   

python

   u_stat, p_value = stats.mannwhitneyu(group1, group2)  # For Mann-Whitney U test
   

python

   from scipy.stats import chi2_contingency
   contingency_table = [[success_group1, failure_group1], [success_group2, failure_group2]]
   chi2, p, dof, expected = chi2_contingency(contingency_table)
   

python

   import pandas as pd
   import matplotlib.pyplot as plt
   import seaborn as sns
   
   df = pd.read_csv('your_file.csv')
   

python

   print(df.columns)
   

python

   # Using seaborn
   plt.figure(figsize=(10, 6))
   sns.scatterplot(data=df, x='variable_x', y='variable_y', hue='category_column')  # Replace with your actual column names
   plt.title('Scatter Plot of Variable X vs Variable Y')
   plt.xlabel('Variable X')
   plt.ylabel('Variable Y')
   plt.legend(title='Category')
   plt.show()
   
   # Using matplotlib
   plt.scatter(df['variable_x'], df['variable_y'], c=df['category_column'], cmap='viridis')
   plt.title('Scatter Plot of Variable X vs Variable Y')
   plt.xlabel('Variable X')
   plt.ylabel('Variable Y')
   plt.colorbar(label='Category')
   plt.show()