Spaces:
Running
Running
Update app.py
Browse files
app.py
CHANGED
|
@@ -52,7 +52,7 @@ def init_baselines():
|
|
| 52 |
matchups = raw_display[['Team', 'Opp', 'FL1$', 'FL2$', 'FL3$', 'Team Total', 'Game Pace', 'SF', 'o_SA', 'SF_m', 'HDCF',
|
| 53 |
'o_HDCA', 'HDCF_m']]
|
| 54 |
data_cols = matchups.columns.drop(['Team', 'Opp'])
|
| 55 |
-
|
| 56 |
|
| 57 |
worksheet = sh.worksheet('Marketshares')
|
| 58 |
raw_display = pd.DataFrame(worksheet.get_values())
|
|
@@ -63,7 +63,7 @@ def init_baselines():
|
|
| 63 |
overall_ms = raw_display[['Line', 'SK1', 'SK2', 'SK3', 'Cost', 'Team Total', 'Shots', 'HDCF', 'Goals', 'Assists', 'Blocks',
|
| 64 |
'L14_Shots', 'L14_HDCF', 'L14_Goals', 'L14_Assists', 'L14_Blocks']]
|
| 65 |
data_cols = overall_ms.columns.drop(['Line', 'SK1', 'SK2', 'SK3'])
|
| 66 |
-
|
| 67 |
|
| 68 |
return matchups, overall_ms
|
| 69 |
|
|
@@ -82,7 +82,7 @@ with col1:
|
|
| 82 |
with col2:
|
| 83 |
if split_var1 == 'Slate Matchups':
|
| 84 |
display_table = matchups
|
| 85 |
-
st.dataframe(display_table, use_container_width = True)
|
| 86 |
st.download_button(
|
| 87 |
label="Export Matchups",
|
| 88 |
data=convert_df_to_csv(display_table),
|
|
@@ -91,7 +91,7 @@ with col2:
|
|
| 91 |
)
|
| 92 |
elif split_var1 == 'Line Marketshares':
|
| 93 |
display_table = overall_ms
|
| 94 |
-
st.dataframe(display_table, use_container_width = True)
|
| 95 |
st.download_button(
|
| 96 |
label="Export Marketshares",
|
| 97 |
data=convert_df_to_csv(display_table),
|
|
|
|
| 52 |
matchups = raw_display[['Team', 'Opp', 'FL1$', 'FL2$', 'FL3$', 'Team Total', 'Game Pace', 'SF', 'o_SA', 'SF_m', 'HDCF',
|
| 53 |
'o_HDCA', 'HDCF_m']]
|
| 54 |
data_cols = matchups.columns.drop(['Team', 'Opp'])
|
| 55 |
+
matchups[data_cols] = matchups[data_cols].apply(pd.to_numeric, errors='coerce')
|
| 56 |
|
| 57 |
worksheet = sh.worksheet('Marketshares')
|
| 58 |
raw_display = pd.DataFrame(worksheet.get_values())
|
|
|
|
| 63 |
overall_ms = raw_display[['Line', 'SK1', 'SK2', 'SK3', 'Cost', 'Team Total', 'Shots', 'HDCF', 'Goals', 'Assists', 'Blocks',
|
| 64 |
'L14_Shots', 'L14_HDCF', 'L14_Goals', 'L14_Assists', 'L14_Blocks']]
|
| 65 |
data_cols = overall_ms.columns.drop(['Line', 'SK1', 'SK2', 'SK3'])
|
| 66 |
+
overall_ms[data_cols] = overall_ms[data_cols].apply(pd.to_numeric, errors='coerce')
|
| 67 |
|
| 68 |
return matchups, overall_ms
|
| 69 |
|
|
|
|
| 82 |
with col2:
|
| 83 |
if split_var1 == 'Slate Matchups':
|
| 84 |
display_table = matchups
|
| 85 |
+
st.dataframe(display_table.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
|
| 86 |
st.download_button(
|
| 87 |
label="Export Matchups",
|
| 88 |
data=convert_df_to_csv(display_table),
|
|
|
|
| 91 |
)
|
| 92 |
elif split_var1 == 'Line Marketshares':
|
| 93 |
display_table = overall_ms
|
| 94 |
+
st.dataframe(display_table.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
|
| 95 |
st.download_button(
|
| 96 |
label="Export Marketshares",
|
| 97 |
data=convert_df_to_csv(display_table),
|