0

I have written a code to create a data table and save it as an image. Here is the code-

df_avg = data_rtm_market.groupby('date').mean()

base = datetime.datetime.today()

date_list = [base - datetime.timedelta(days=x) for x in range(1,8)]
dtr = [x.strftime("%d-%m-%Y") for x in date_list]

df_avg.reset_index(inplace=True)
last_7_day = df_avg[df_avg['date'].isin(dtr)]

data_rtm_market.date = pd.to_datetime(data_rtm_market.date,format="%d-%m-%Y")

dam_market.date = pd.to_datetime(dam_market.date,format="%d-%m-%Y")

final_ = pd.merge(data_rtm_market,dam_market,how='inner', on=['date','block'])

df = final_

df[['total_purchase_bid','total_sell_bid','total_cleared_vol']]=df.groupby('date')['purchase_bid', 'sell_bid', 'cleared_volume'].transform('sum')
df[['max_mcp_rtm', 'max_mcp_dam']]=final_.groupby('date')['mcp_x','mcp_y'].transform('max')
df[['avg_mcp_rtm','avg_mcp_dam']]=final_.groupby('date')['mcp_x','mcp_y'].transform('mean')
df[['min_mcp_rtm','min_mcp_dam']]=final_.groupby('date')['mcp_x','mcp_y'].transform('min')

summary = df[['date','total_purchase_bid',
       'total_sell_bid', 'total_cleared_vol', 'max_mcp_rtm', 'max_mcp_dam',
       'avg_mcp_rtm', 'avg_mcp_dam', 'min_mcp_rtm', 'min_mcp_dam']]

table = summary.drop_duplicates(keep='first')

table1 = table.tail(8)
table1 = table1.iloc[:-1]

col3 = table1[['total_purchase_bid',
       'total_sell_bid', 'total_cleared_vol']].apply(lambda x:round((x/4000),2))

col4 = table1[['max_mcp_rtm', 'max_mcp_dam',
               'avg_mcp_rtm', 'avg_mcp_dam', 'min_mcp_rtm', 'min_mcp_dam']].apply(lambda x:round(x,2))

final_temp = pd.concat([table1['date'],col3, col4], axis=1, sort=False)

final_temp['date'] = final_temp['date'].dt.strftime('%d-%m-%Y')

final_temp = final_temp.set_index('date').T

final_temp.reset_index(inplace=True,drop=True)


final_temp.insert(0,'1', ["Volume(MUs)","",""," Price(Rs/kWh)","","Price(Rs/kWh)","","Price(Rs/kWh)",""])
final_temp.insert(1,'2', ["Buy    ","Sell    ","Cleared","Max RTM","Max DAM","Avg RTM","Avg DAM","Min RTM","Min DAM"])


def render_mpl_table(data, col_width=3.0, row_height=0.825, font_size=26,
                     header_color='#5DADE2', row_colors=['#f1f1f2', 'w'], edge_color='black',
                     bbox=[0, 0, 1, 1], header_columns=0,
                     ax=None, **kwargs):
    if ax is None:
        size = (np.array(data.shape[::-1]) + np.array([0, 1])) * np.array([col_width, row_height])
        fig, ax = plt.subplots(figsize=size)
        ax.axis('off')

    mpl_table = ax.table(cellText=data.values, bbox=bbox, colLabels=data.columns, **kwargs)

    mpl_table.auto_set_font_size(False)
    mpl_table.set_fontsize(font_size)

    for k, cell in six.iteritems(mpl_table._cells):
        cell.set_edgecolor(edge_color)
        if k[0] == 0 or k[1] < header_columns:
            cell.set_text_props(weight='bold', color='white')
            cell.set_facecolor(header_color)
        else:
            cell.set_facecolor(row_colors[k[0]%len(row_colors) ])
    return fig

final_temp.columns.values[0] = ""
final_temp.columns.values[1] = ""
fig = render_mpl_table(final_temp, header_columns=0, col_width=4)
fig.savefig("/content/assets/summary_table.jpeg",bbox_inches='tight')

This is giving me the data table as below- enter image description here

I want to use conditional formatting in the cells such that, for example if Max RTM is greater than Max DAM, then the cell background or cell text turns green, and if Max RTM is less than Max DAM, the cell background or cell text turns red. Any way of doing it? Also how can I merge the text in the first column?

Pooja Bhateley
  • 67
  • 6

1 Answers1

0

You can do the following if you transpose your table:

import pandas as pd

#Initialize random dataframe
df_test = pd.DataFrame(np.random.normal(size = (10,2)), 
columns=['A', 'B'])

#Style functions
def compare_columns(data, column1, column2, color1, color2):
    attr = 'background-color: {}'
    if data[column1] > data[column2]:
        return [attr.format(color1) for s in data]
    elif data[column1] <= data[column2]:
        return [attr.format(color2) for s in data]

df_test.style.apply(compare_columns, column1 = 'A', 
                    column2 = 'B', color1 = 'red', 
                    color2 = 'green', axis=1)

Output:

enter image description here

See answer here (Coloring Cells in Pandas) and pandas docs (https://pandas.pydata.org/pandas-docs/stable/user_guide/style.html) for more details.

perlusha
  • 153
  • 6