ElisonSherton

import pandas as pd
data = pd.read_csv('MyAnimeList/anime.csv', na_values = ['Unknown'])
# Have a look at missing values and the total records in the data
print(data.isnull().sum()/len(data) * 100)
print(f"Total number of records: {len(data)}")

ElisonSherton

# Have a look at missing values and the total records in the  
# data after dropping records with missing data
data = data.dropna(axis = 0)
print(data.isnull().sum()/len(data) * 100)
display(HTML(data.head(5).to_html()))
print(f"Total number of records: {len(data)}")

ElisonSherton

@classmethod
def histogram(self, df, colname, bins = None):
    # Create an axis object using subplots from matplotlib
    fig, ax = plt.subplots(1, 1 ,figsize = (20, 6))

    # Plot a histogram of the column from the dataframe mentioned
    if bins:
        sns.distplot(df[colname], ax = ax, bins = bins)
    else:
        sns.distplot(df[colname], ax = ax)

ElisonSherton

@classmethod
def genre_countplot(self, df, colname):
    # Process the genre column to split on comma and append resulting
    # genres all to a single list
    all_genres = []
    for item in df[colname]:
        item = item.strip()
        all_genres.extend(item.split(', '))

    # Count the number of items in the genre and create a dataframe

ElisonSherton

@classmethod
def barplot(self, df, xcolname, ycolname):
    # Create an axis object in which to place our barchart
    fig, ax = plt.subplots(1, 1, figsize = (15, 8))

    # Plot the barchart
    sns.barplot(x = xcolname, y = ycolname, data = df, ax = ax)

    # Set the axes and title of the plot
    title = f"Barchart of {xcolname.title()} vs {ycolname.title()}"

ElisonSherton

@classmethod
def pieplot(self, data, colname):
    # Create an axis object
    fig, ax = plt.subplots(1, 1, figsize = (15, 8))

    # Get the data in the right format
    labels = list(data.keys())
    sizes  = list(data.values())

    # Plot the pie chart

ElisonSherton

@classmethod
def boxplot(self, data, xcolname, ycolname, ax, log=False):
    # Create a copy of the data to work with
    df = data.copy()

    # Check on the log condition
    name = None
    if log:
        name = f'Log_{ycolname}'
        df[name] = np.log(df[ycolname])

ElisonSherton

@classmethod
def violinplot(self, data, xcolname, ycolname, ax, log=False):
    # Create a copy of the data to work with
    df = data.copy()

    # Check on the log condition
    name = None
    if log:
        name = f'Log_{ycolname}'
        df[name] = np.log(df[ycolname])

ElisonSherton

@classmethod
def scatterplot(self, data, xcolname, ycolname):
    # Create an axis object to plot on
    fig, ax = plt.subplots(1, 1, figsize = (15, 8))

    # Plot the scatterplot
    sns.regplot(x = xcolname, y = ycolname, ax = ax, data = data)

    # Set the title and axes names
    title = f"Scatterplot of {ycolname.title()} vs {xcolname.title()}"

ElisonSherton

@classmethod
def heatmap(self, data, xcolname, ycolname, cmap = 'YlGnBu', quantity = "occurrences"):
    # Create an axis object to plot on
    fig, ax = plt.subplots(1, 1, figsize = (20, 15))

    # Plot the heatmap
    sns.heatmap(data, ax = ax, annot = True, cmap = cmap, fmt=".2f", linewidth=0.3)

    # Set the title and name the axes
    title = f"Heatmap describing {quantity}: {ycolname.title()} vs {xcolname.title()}"