How to use ChatGPT for Analytics Specialist Job Role

python

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

python

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

python

   # Check for missing values
   print(df.isnull().sum())
   # Fill missing values with the mean (for numerical columns)
   df['column_name'].fillna(df['column_name'].mean(), inplace=True)
   # Drop rows with missing values
   df.dropna(inplace=True)
   

python

   df.drop_duplicates(inplace=True)
   

python

   df['date_column'] = pd.to_datetime(df['date_column'])
   

python

   df['category_column'] = df['category_column'].str.lower().str.strip()
   

python

   from scipy import stats
   df = df[(np.abs(stats.zscore(df['numerical_column'])) < 3)]
   

python

   print(df.info())
   print(df.describe())
   

python

   from scipy import stats
   group1 = df[df['group'] == 'A']['value']
   group2 = df[df['group'] == 'B']['value']
   t_stat, p_value = stats.ttest_ind(group1, group2)
   print(f'T-statistic: {t_stat}, P-value: {p_value}')
   

python

   from scipy import stats
   f_stat, p_value = stats.f_oneway(df[df['group'] == 'A']['value'], df[df['group'] == 'B']['value'], df[df['group'] == 'C']['value'])
   print(f'F-statistic: {f_stat}, P-value: {p_value}')
   

python

   correlation = df['variable1'].corr(df['variable2'])
   print(f'Correlation: {correlation}')
   

python

   contingency_table = pd.crosstab(df['category1'], df['category2'])
   chi2, p, dof, expected = stats.chi2_contingency(contingency_table)
   print(f'Chi-squared: {chi2}, P-value: {p}')
   

python

   import statsmodels.api as sm
   X = df[['predictor1', 'predictor2']]  # Independent variables
   y = df['outcome']  # Dependent variable
   X = sm.add_constant(X)  # Adds a constant term to the predictor
   model = sm.OLS(y, X).fit()
   print(model.summary())
   

python

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

python

   summary_df = df.groupby('category')['sales'].mean().reset_index()
   

python

   plt.figure(figsize=(10, 6))
   sns.barplot(x='category', y='sales', data=summary_df)
   plt.title('Average Sales by Category')
   plt.xlabel('Category')
   plt.ylabel('Average Sales')
   plt.xticks(rotation=45)
   plt.tight_layout()
   plt.show()
   

python

   import pandas as pd
   from sklearn.model_selection import train_test_split
   from sklearn.linear_model import LinearRegression
   from sklearn.metrics import mean_squared_error, r2_score
   df = pd.read_csv('your_file.csv')
   

python

   X = df[['feature1', 'feature2', 'feature3']]  # Independent variables
   y = df['target_variable']  # Dependent variable
   

python

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

python

   model = LinearRegression()
   model.fit(X_train, y_train)
   

python

   y_pred = model.predict(X_test)
   

python

   mse = mean_squared_error(y_test, y_pred)
   r2 = r2_score(y_test, y_pred)
   print(f'Mean Squared Error: {mse}')
   print(f'R-squared: {r2}')