git-shogg

# --- Map tickers into the quarterly holding dataframes ---
all_this_qtr_holdings['Ticker'] = all_this_qtr_holdings['CUSIP'].map(dict(cusip_mappings))
all_last_qtr_holdings['Ticker'] = all_last_qtr_holdings['CUSIP'].map(dict(cusip_mappings))
all_this_qtr_holdings['Ticker'] = all_this_qtr_holdings['Ticker'].fillna('N/A')
all_last_qtr_holdings['Ticker'] = all_last_qtr_holdings['Ticker'].fillna('N/A')

git-shogg

# --- Functions to support pandas lambda function ---
def calc_pct_bought_sold(this_share_count, last_share_count):
    if this_share_count > 0 and last_share_count == 0:
        return 100
    elif this_share_count == 0 and last_share_count == 0:   # In the rare circumstances that both this share count and last share count are zero return 0.
        return 0
    else:
        return ((this_share_count/last_share_count)-1)*100
 
def calc_dollar_bought_sold(count_bought_sold, last_holding_val, last_share_count, this_holding_val, this_share_count):

git-shogg

def sns_heatmap(dataframe, key_column, key_column_format, chart_title, c_pos_neg):
    # Build out heatmap things
    dataframe['Position'] = range(1,len(dataframe) + 1)
    dataframe['y'] = [(x//10 + 1 if x%10 != 0 else (x//10)) for x in dataframe['Position']]
    dataframe['x'] = [(x%10 if x%10 != 0 else 10) for x in dataframe['Position']]
    # most_bought['normalized_dollars_bought_sold'] = (most_bought['dollars_bought_sold'] - most_bought['dollars_bought_sold'].min()) / (most_bought['dollars_bought_sold'].max()-most_bought['dollars_bought_sold'].min())
    # most_bought['natural_log'] = np.log(most_bought['normalized_dollars_bought_sold'])
    pivot_df = dataframe.pivot(index='y', columns='x', values=key_column)
    
    ticker_labels = np.asarray(dataframe['Ticker']).reshape((10,10))