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C/A | UNIT | SCP | STATION | LINENAME | DIVISION | DATE | TIME | DESC | ENTRIES | EXITS | ||
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | A002 | R051 | 02-00-00 | 59 ST | NQR456W | BMT | 2020-08-22 | 00:00:00 | REGULAR | 7447810 | 2532191 | |
1 | A002 | R051 | 02-00-00 | 59 ST | NQR456W | BMT | 2020-08-22 | 04:00:00 | REGULAR | 7447812 | 2532197 | |
2 | A002 | R051 | 02-00-00 | 59 ST | NQR456W | BMT | 2020-08-22 | 08:00:00 | REGULAR | 7447824 | 2532208 | |
3 | A002 | R051 | 02-00-00 | 59 ST | NQR456W | BMT | 2020-08-22 | 12:00:00 | REGULAR | 7447852 | 2532248 | |
4 | A002 | R051 | 02-00-00 | 59 ST | NQR456W | BMT | 2020-08-22 | 16:00:00 | REGULAR | 7447937 | 2532276 |
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df['ENTRY_DELTA'][df['ENTRY_DELTA']>10000]=np.nan | |
df['ENTRY_DELTA'][df['ENTRY_DELTA']<0]=np.nan | |
# Setting the anomaly values due to reset of counters to the uniform NaN values | |
delta_list = list(df['ENTRY_DELTA']) | |
ind = 0 | |
for i in delta_list: | |
if np.isnan(i) == 1: | |
delta_list[ind] = np.nanmean([delta_list[ind-1],delta_list[ind+1]]) | |
ind += 1 |
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group_station = df.groupby('STATION')['ENTRY_EXIT'].sum().sort_values(ascending=False) | |
fig1 = plt.figure(figsize=[8,6]) | |
ax1 = sns.distplot(group_station,bins=50,kde=False) | |
plt.xlim([0,410000]) | |
plt.ylabel('No. of Stations',fontsize=15, weight='bold') | |
plt.xlabel('Total Traffic', fontsize=15, weight='bold') | |
ax1.annotate('Top 10 Stations', xy=(0.73, 0.08), xytext=(0.73, 0.12), xycoords='axes fraction', | |
fontsize=12, ha='center', va='bottom', | |
arrowprops=dict(arrowstyle='-[, widthB=9.0, lengthB=1', lw=1.0),color='blue') |
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plt.figure(figsize=[8,5]) | |
ax = sns.barplot(data=group_station.head(10).reset_index(),x='ENTRY_EXIT',y='STATION',palette='rainbow') | |
plt.xlabel('Total Traffic',weight='bold',fontsize=15) | |
plt.ylabel('Top 10 Stations',weight='bold',fontsize=15) | |
plt.xticks(range(0,400001,50000),[str(int(i/1000))+'k' for i in range(0,400001,50000)]) | |
plt.title('Busiest MTA Stations from 22/8 to 28/8', weight='bold',fontsize='15') | |
for p in ax.patches: | |
ax.annotate(str(int(p.get_width()/1000))+'k', (p.get_width(), p.get_y()+0.5)) | |
sns.despine() |
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df_10 = df[df['STATION'].isin(list(group_station.head(10).index))] | |
df_10['WEEKDAY'] = df_10['DATE'].dt.day_name() | |
group_station_day = df_10.groupby(['STATION','WEEKDAY'])['ENTRY_EXIT'].sum() | |
matrix_station_day = group_station_day.unstack() | |
matrix_station_day.reset_index() | |
matrix_station_day = matrix_station_day.reindex(columns=["Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday"]) | |
matrix_station_day = matrix_station_day.reindex(index=list(group_station.head(10).index)) | |
array = np.array(matrix_station_day.applymap(lambda x:str(round(x/1000,1))+'k')) | |
fig2 = plt.figure(figsize=[10,10]) |
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def timeperiod(time): | |
if time >= datetime.time(0,0,0) and time < datetime.time(4,0,0): | |
return "12am-4am" | |
elif time >= datetime.time(4,0,0) and time < datetime.time(8,0,0): | |
return "4am-8am" | |
elif time >= datetime.time(8,0,0) and time < datetime.time(12,0,0): | |
return "8am-12pm" | |
elif time >= datetime.time(12,0,0) and time < datetime.time(16,0,0): | |
return "12pm-4pm" | |
elif time >= datetime.time(16,0,0) and time < datetime.time(20,0,0): |
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matrix_list= [] | |
for station in list(group_station.head(10).index): | |
df_station = df_10[df_10['STATION']==station] | |
group_day_time = df_station.groupby(['WEEKDAY','TIME_PERIOD'])['ENTRY_EXIT'].sum() | |
matrix_day_time = group_day_time.unstack() | |
matrix_day_time.reset_index() | |
matrix_day_time = matrix_day_time.reindex(index=["Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday"]) | |
matrix_day_time = matrix_day_time.reindex(columns=["12am-4am","4am-8am","8am-12pm","12pm-4pm","4pm-8pm","8pm-12am"]) | |
matrix_list.append(matrix_day_time) |
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import numpy as np | |
from bs4 import BeautifulSoup | |
import requests | |
player_list = [] | |
url = 'https://www.fifaindex.com/players/{}/' #creating url for primary pages | |
pageno = 1 | |
while True: |
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