#Dataset Preview
df.head()

#Preview of dataframe with all zscores

newdf.head()

#Creating a new dataframe with all attributes
newdf.drop(newdf.columns.difference(['PLAYER NUMBER NAME','Competition','Season','Minutes Score - (15%)','Defending Score - (20%)','Aerial Score - (7.5%)','Crossing Score - (10%)','Dribbling Score - (15%)','Creativity Score - (20%)','BallQuality Score - (12.5%)']), 1, inplace=True)
df3 = newdf
df3.columns = ['Player', 'Competition','Season','PLAYER NUMBER NAME', 'Minutes Score - (15%)', 'Defending Score - (20%)', 'Aerial Score - (7.5%)', 'Crossing Score - (10%)', 'Dribbling Score - (15%)', 'Creativity Score - (20%)', 'BallQuality Score - (12.5%)']
df3 = df3.drop('PLAYER NUMBER NAME', axis=1)

#Round overall attributes

df3 = df3.round({'Minutes Score - (15%)': 2})
df3 = df3.round({'Defending Score - (20%)': 2})
df3 = df3.round({'Aerial Score - (7.5%)': 2})
df3 = df3.round({'Crossing Score - (10%)': 2})
df3 = df3.round({'Dribbling Score - (15%)': 2})
df3 = df3.round({'Creativity Score - (20%)': 2})
df3 = df3.round({'BallQuality Score - (12.5%)': 2})

df3.head()

#Rating Breakdown visual

#get parameters
params = list(dfviz.columns)
print(params)

#drop the first list team because we don't need player name. Start at first metric
params = params[1:]

#Input player name or number here
values = dfviz.loc[dfviz['Player']==77].reset_index()
values = list(values.loc[0])
values = values[2:]


#Getting the min and max values for the ranges for the data visual

min_range = []
max_range = []

for x in params:
    a = min(dfviz[params][x])
    
    b = max(dfviz[params][x])

    min_range.append((a))
    max_range.append((b))
    
#Creating pizza chart for rating breakdown visual

baker = PyPizza(
    params=params,
    background_color="white", straight_line_color="#000000",
    min_range=min_range,        # min range values
    max_range=max_range,        # max range values
    last_circle_color="#000000", last_circle_lw=2.5, straight_line_lw=1,
    other_circle_lw=0, other_circle_color="#000000", inner_circle_size=16.5,
)


# plot pizza
fig, ax = baker.make_pizza(
    values,                          # list of values
    figsize=(10, 10),                # adjust figsize according to your need
    color_blank_space="same",        # use same color to fill blank space
    blank_alpha=0.4,                 # alpha for blank-space colors
    kwargs_slices=dict(
        facecolor="#660000",edgecolor="white", zorder=2, linewidth=1
    ),                               # values to be used when plotting slices
    kwargs_params=dict(
        color="#000000", fontsize=13,
    ),                               # values to be used when adding parameter
    kwargs_values=dict(
        color="white", fontsize=13,
        bbox=dict(
            edgecolor="white", facecolor="#660000",
            boxstyle="round,pad=0.2", lw=1
        )
    )                                # values to be used when adding parameter-values
)

# Add Title / Edit Player Name and Club
fig.text(
    0.515, 0.975, "Player Number 77 - Premiership 2021/2022", size=18, fontweight='bold',
    ha="center", color="#000000"
)

# Add Subtitle
fig.text(
    0.515, 0.956,
    "Fullback / Wingback Rating Breakdown | Hearts Dataset",
    size=14,
    ha="center", color="#000000"
)

# add credits
CREDIT_1 = "Graphic: @HenshawAnalysis"
CREDIT_2 = "Data is via Statsbomb"
CREDIT_3 = "Template: Fullback / Wingback Rating Breakdown"
CREDIT_4 = "Notes: Value in brackets is the weighting for that attribute"

fig.text(
    0.935, 0.000, f"{CREDIT_1}\n{CREDIT_2}\n{CREDIT_3}\n{CREDIT_4}", size=9,
    color="#545454",
    ha="right"
)

fig.text (0.511, 0.509, "Overall\nRating", size=12, ha="center", fontweight='bold')
fig.text (0.511, 0.48, "65.31", size=16, ha="center", fontweight='bold')



# add image

im2 = imageio.imread('HeartsLogo.png')
ax_image = add_image(
    im2, fig, left=0.09, bottom=-0.01, width=0.13, height=0.127
)   # these values might differ when you are plotting

plt.show()

#Weight the overall ratings
df3['Minutes Score - (15%)'] = df3['Minutes Score - (15%)'] *0.15
df3['Defending Score - (20%)'] = df3['Defending Score - (20%)'] *0.2
df3['Aerial Score - (7.5%)'] = df3['Aerial Score - (7.5%)'] *0.075
df3['Crossing Score - (10%)'] = df3['Crossing Score - (10%)'] *0.1
df3['Dribbling Score - (15%)'] = df3['Dribbling Score - (15%)'] *0.15
df3['Creativity Score - (20%)'] = df3['Creativity Score - (20%)'] *0.2
df3['BallQuality Score - (12.5%)'] = df3['BallQuality Score - (12.5%)'] *0.125



df3['FBWB_Rating'] = df3['Minutes Score - (15%)'] + df3['Defending Score - (20%)'] + df3['Aerial Score - (7.5%)'] + df3['Crossing Score - (10%)'] + df3['Creativity Score - (20%)'] + df3['BallQuality Score - (12.5%)']

#Round overall ratings
df3 = df3.round({'FBWB_Rating': 2})

df3.sort_values("FBWB_Rating", ascending=False)