
# bar chart for given grading component and given student name all blue except student in red
import pandas as pd


df = pd.read_csv("grades.csv")
df


weights = df.iloc[0] 
maxpoints = df.iloc[1]
scores = df[2:]
scores.reset_index(inplace=True)
scores = scores.drop('index',axis=1)
scores["AVG"] = weights.loc["PROJS"]*scores['PROJS']/maxpoints.loc["PROJS"] + \
                    weights.loc["MT"]*scores['MT']/maxpoints.loc["MT"] + \
                    weights.loc["FINAL"]*scores['FINAL']/maxpoints.loc["FINAL"]
print(scores)

       SNAME  PROJS  MT  FINAL        AVG
0      Alice    578  64    126  85.159168
1        Bob    620  84    123  93.059211
2      Carol    578  42    133  81.402589
3      Cyrus    620  65    120  88.684211
4      David    620  76    152  95.000000
5     Donald    620  83    148  95.973684
6     Esther    620  75    160  95.789474
7     George    496  48     70  66.855263
8     Haslem    620  68    138  91.565789
9      Henry    620  69    108  88.026316
10    Howard    526  82    108  81.666384
11    Joseph    620  70    146  92.986842
12       Ken    617  70     25  77.571520
13    Lohith    601  73    111  87.404711
14    Manuel    620  73    148  93.868421
15    Morgan    620  76    148  94.500000
16    Muncie    620  67    113  88.230263
17    Nathan    620  70    116  89.236842
18    Norrie    593  68      0  71.702886
19      Opal    620  75    142  93.539474
20     Peter    620  79    148  95.131579
21       Pim    610  65    148  91.216469
22   Podesta    620  65    148  92.184211
23       Pug    578  56     89  78.849958
24      Rain    549  49    120  78.444822
25       Raj    620  58    134  88.960526
26    Raskin    600  66      0  71.959253
27    Reefer    603  80    118  89.946944
28     Right    620  78    134  93.171053
29     Round    561  78    140  88.211375
30    Ruskin    620  69    148  93.026316
31       Sam    514  92    153  88.235357
32     Singh    298   0      0  28.838710
33   Solomon    600  89    134  93.551358
34     Trent    620  67    143  91.980263
35  Veronica    620  79    128  92.631579
36    Walter    620  77    126  91.960526
37     Wendy    620  80    148  95.342105
38    Xavier    620  73    148  93.868421
39       Zoe    586  68    140  88.525467


scores[["PROJS","MT","FINAL","AVG"]].mean()

PROJS    593.700000
MT        69.150000
FINAL    119.550000
AVG       86.956483
dtype: float64


import plotly.express as px
import plotly.graph_objects as go
import numpy as np

sname = "Raj"
compname = "FINAL"

comp = scores[["SNAME",compname]].sort_values(by=compname,ascending=False)
colors = list(np.where(comp["SNAME"]==sname,'red','blue'))
comp['category'] = [str(i) for i in comp.index]  # values to cycle through for plotly to change colors

fig = px.bar(comp,x="SNAME",y=compname, color='category',color_discrete_sequence=colors)
fig.add_trace(go.Scatter(x=comp["SNAME"],
                         y=comp[compname],
                         text=comp[compname],
                         mode='text',
                         textposition='top center'))
fig.update_layout(showlegend=False)
fig.update_layout(
    font=dict(
        family="Arial, monospace",
        size=12
    )
)
fig.show()


def plot_given_sname_compname(sname,compname):
    comp = scores[["SNAME",compname]].sort_values(by=compname,ascending=False)
    colors = list(np.where(comp["SNAME"]==sname,'red','blue'))
    comp['category'] = [str(i) for i in comp.index]  # values to cycle through for plotly to change colors

    fig = px.bar(comp,x="SNAME",y=compname, color='category',color_discrete_sequence=colors)
    fig.add_trace(go.Scatter(x=comp["SNAME"],
                             y=comp[compname],
                             text=comp[compname],
                             mode='text',
                             textposition='top center'))
    fig.update_layout(showlegend=False)
    fig.update_layout(
        font=dict(
            family="Arial, monospace",
            size=12
        )
    )
    fig.show()


plot_given_sname_compname("Raj","MT")


plot_given_sname_compname("Raj","FINAL")


plot_given_sname_compname("Esther","MT")

	SNAME	PROJS	MT	FINAL
0	DUMMY	60	20	20
1	DUMMY	620	95	160
2	Alice	578	64	126
3	Bob	620	84	123
4	Carol	578	42	133
5	Cyrus	620	65	120
6	David	620	76	152
7	Donald	620	83	148
8	Esther	620	75	160
9	George	496	48	70
10	Haslem	620	68	138
11	Henry	620	69	108
12	Howard	526	82	108
13	Joseph	620	70	146
14	Ken	617	70	25
15	Lohith	601	73	111
16	Manuel	620	73	148
17	Morgan	620	76	148
18	Muncie	620	67	113
19	Nathan	620	70	116
20	Norrie	593	68	0
21	Opal	620	75	142
22	Peter	620	79	148
23	Pim	610	65	148
24	Podesta	620	65	148
25	Pug	578	56	89
26	Rain	549	49	120
27	Raj	620	58	134
28	Raskin	600	66	0
29	Reefer	603	80	118
30	Right	620	78	134
31	Round	561	78	140
32	Ruskin	620	69	148
33	Sam	514	92	153
34	Singh	298	0	0
35	Solomon	600	89	134
36	Trent	620	67	143
37	Veronica	620	79	128
38	Walter	620	77	126
39	Wendy	620	80	148
40	Xavier	620	73	148
41	Zoe	586	68	140