dfuller22

def plot_importance(tree, X_train, top_n=10, figsize=(10,10), ax=None):
    
    """Takes in pre-fit descision tree and the training X data used. Will output
    a horizontal bar plot (.plt) of the top 10 (default) features used in said tree."""
    
    ## Imports
    import pandas as pd
    import matplotlib as plt

    ## Generate feature importances + store into series with correct column names 

dfuller22

def regressor_tester(reg_, X_tr, X_te, y_tr, y_te, verbose=False, display_res=False, keep_preds=False):
    import pandas as pd
    import numpy as np
    from sklearn import metrics
    
    ## Check if multiple regressors to check
    if isinstance(reg_, list):
        ## Container for multiple regressor results + counter
        count = 0
        holder = []

dfuller22

def cand_per_yr_viewer(party_dict, yr_start='1920', yr_end=None, bar_width=0.15, figsize_=(10,7), keep=False):
    import matplotlib.pyplot as plt
    import numpy as np
    
    ## This code was heavily influenced by that found at:
    # https://matplotlib.org/3.1.0/gallery/lines_bars_and_markers/barchart.html
    ## Setting dictionaries for label positioning
    ha = {'center': 'center', 'right': 'left', 'left': 'right'}
    offset = {'center': 0, 'right': 1, 'left': -1}
    xpos = 'center'