DL12L3W

# Functions
def check_nan_values(df):
    nan_columns = df.columns[df.isnull().any()].tolist()

    if not nan_columns:
        print("No NaN values found")
    else:
        print("NaN values found in: ")
        for column in nan_columns:
            print(f"{column}: {df[column].isnull().sum()} NaN values")

def bar_plot(variable):
    # get feature
    var = train_df[variable]
    varValue = var.value_counts()

    # visualize
    plt.figure(figsize = (9,3))
    plt.bar(varValue.index, varValue)
    plt.xticks(varValue.index, varValue.index.values)
    plt.ylabel("Frequency")
    plt.title(variable)
    plt.show()
    print("{}: \n {}".format(variable,varValue))

def pie_plot(variable):
    # get feature
    var = train_df[variable]
    varValue = var.value_counts()

    varValue.plot.pie(autopct='%1.1f%%', textprops={'fontsize':12}).set_title("Frequency")
    print("{}: \n {}".format(variable,varValue))

df['column'] = df['column'].fillna(value)

df.drop('a', axis=1, inplace=True)

df[['A', 'B']] = df['AB'].str.split(' ', n=1, expand=True)

df.iloc[0:2, df.columns.get_loc('Taste')] = 'good' (row, col)

# Select Columns 'B' through 'D'
selected_columns = df.loc[:, 'B':'D']

# Select Rows Between 'Row_2' and 'Row_4'
selected_rows = df.loc['Row_2':'Row_4']