Let's say, I have this specific dataframe below.
import pandas as pd
import numpy as np
periods = 46670
dates = pd.date_range(start='2005-07-01', end='2021-06-01', periods=periods)
operating_airline = ['Lufthansa','Air Canada','American Air','American Pan Pacific','Emirates','KLM','Scandinavian Air',
'Singapore Airlines','Japan Air','Air Force 1']
geo_summary = ['Domestic', 'International']
geo_region = ['US','Canada','South America', 'Europe','Nothern Europe']
np.random.seed(2002) # for repeatable values
operating_airline = np.random.choice(operating_airline, size=periods)
geo_summary = np.random.choice(geo_summary, size=periods)
geo_region = np.random.choice(geo_region, size=periods)
passenger_count = np.random.randint(1000,10000, size=periods)
test = pd.DataFrame({'Dates':dates,'Operating_Airlines':operating_airline,'Geo_Summary':geo_summary,'Geo_Region':geo_region,'Passenger_Count':passenger_count})
# display(test.head())
Dates Operating_Airlines Geo_Summary Geo_Region Passenger_Count
0 2005-07-01 00:00:00.000000000 Air Canada Domestic South America 9958
1 2005-07-01 02:59:23.667530909 American Air Domestic Europe 7853
2 2005-07-01 05:58:47.335061818 Japan Air International Canada 3162
3 2005-07-01 08:58:11.002592727 Air Force 1 International South America 5100
4 2005-07-01 11:57:34.670123636 Japan Air International Canada 5382
What i've been trying to achieve mostly;
Note: The bars should have annotations(although im aware of how to do this part, id still like see a different approach. if there is).
My issue was that Im unable to customise Dates format(e.g. y-m-d) & date range(6month, a year) simultaneously plotting 2 variables Air Canada and American Pan Pacific in the Operating_Airline using only Pandas &/or Matplotlib. Im open to all types of answers ofcourse!
How could I customize date ranges further if I decided to plot for a yearly period?
What I've tried(failed & unable to find a solution) to;
plt.bar(test['Date'], test['Operating_Airline'].count(), label='Test', width=20, color=['red'])
plt.bar(test['Date'], test['Operating_Airline'].count(), label='Test_1', width=20)
plt.title('Test')
plt.legend()
Love all the help, Ive been at this for weeks now.
Note:
CodePudding user response：
- Use
DataFrame Views
dfgDates Operating_Airlines counts 0 2005-12 Air Canada 139 1 2005-12 Air Force 1 147 2 2005-12 American Air 138 3 2005-12 American Pan Pacific 148 4 2005-12 Emirates 159 5 2005-12 Japan Air 136 6 2005-12 KLM 137 7 2005-12 Lufthansa 151 8 2005-12 Scandinavian Air 166 9 2005-12 Singapore Airlines 156 10 2006-12 Air Canada 277 11 2006-12 Air Force 1 297 12 2006-12 American Air 299 13 2006-12 American Pan Pacific 286 14 2006-12 Emirates 312 15 2006-12 Japan Air 297 16 2006-12 KLM 312 17 2006-12 Lufthansa 290 18 2006-12 Scandinavian Air 270 19 2006-12 Singapore Airlines 290 20 2007-12 Air Canada 301 21 2007-12 Air Force 1 268 22 2007-12 American Air 267 23 2007-12 American Pan Pacific 297 24 2007-12 Emirates 298 25 2007-12 Japan Air 291 26 2007-12 KLM 295 27 2007-12 Lufthansa 290 28 2007-12 Scandinavian Air 316 29 2007-12 Singapore Airlines 307 30 2008-12 Air Canada 296 31 2008-12 Air Force 1 279 32 2008-12 American Air 286 33 2008-12 American Pan Pacific 316 34 2008-12 Emirates 306 35 2008-12 Japan Air 290 36 2008-12 KLM 286 37 2008-12 Lufthansa 284 38 2008-12 Scandinavian Air 281 39 2008-12 Singapore Airlines 314 40 2009-12 Air Canada 312 41 2009-12 Air Force 1 264 42 2009-12 American Air 329 43 2009-12 American Pan Pacific 276 44 2009-12 Emirates 284 45 2009-12 Japan Air 273 46 2009-12 KLM 313 47 2009-12 Lufthansa 304 48 2009-12 Scandinavian Air 292 49 2009-12 Singapore Airlines 283 50 2010-12 Air Canada 300 51 2010-12 Air Force 1 279 52 2010-12 American Air 291 53 2010-12 American Pan Pacific 292 54 2010-12 Emirates 284 55 2010-12 Japan Air 309 56 2010-12 KLM 303 57 2010-12 Lufthansa 283 58 2010-12 Scandinavian Air 309 59 2010-12 Singapore Airlines 280 60 2011-12 Air Canada 293 61 2011-12 Air Force 1 304 62 2011-12 American Air 279 63 2011-12 American Pan Pacific 330 64 2011-12 Emirates 279 65 2011-12 Japan Air 287 66 2011-12 KLM 286 67 2011-12 Lufthansa 281 68 2011-12 Scandinavian Air 299 69 2011-12 Singapore Airlines 292 70 2012-12 Air Canada 312 71 2012-12 Air Force 1 291 72 2012-12 American Air 276 73 2012-12 American Pan Pacific 312 74 2012-12 Emirates 303 75 2012-12 Japan Air 304 76 2012-12 KLM 271 77 2012-12 Lufthansa 282 78 2012-12 Scandinavian Air 301 79 2012-12 Singapore Airlines 286 80 2013-12 Air Canada 274 81 2013-12 Air Force 1 301 82 2013-12 American Air 298 83 2013-12 American Pan Pacific 283 84 2013-12 Emirates 347 85 2013-12 Japan Air 303 86 2013-12 KLM 270 87 2013-12 Lufthansa 290 88 2013-12 Scandinavian Air 279 89 2013-12 Singapore Airlines 284 90 2014-12 Air Canada 288 91 2014-12 Air Force 1 317 92 2014-12 American Air 312 93 2014-12 American Pan Pacific 296 94 2014-12 Emirates 309 95 2014-12 Japan Air 275 96 2014-12 KLM 273 97 2014-12 Lufthansa 278 98 2014-12 Scandinavian Air 296 99 2014-12 Singapore Airlines 286 100 2015-12 Air Canada 257 101 2015-12 Air Force 1 291 102 2015-12 American Air 305 103 2015-12 American Pan Pacific 279 104 2015-12 Emirates 331 105 2015-12 Japan Air 285 106 2015-12 KLM 320 107 2015-12 Lufthansa 306 108 2015-12 Scandinavian Air 280 109 2015-12 Singapore Airlines 276 110 2016-12 Air Canada 274 111 2016-12 Air Force 1 292 112 2016-12 American Air 272 113 2016-12 American Pan Pacific 322 114 2016-12 Emirates 309 115 2016-12 Japan Air 281 116 2016-12 KLM 263 117 2016-12 Lufthansa 305 118 2016-12 Scandinavian Air 328 119 2016-12 Singapore Airlines 292 120 2017-12 Air Canada 291 121 2017-12 Air Force 1 263 122 2017-12 American Air 298 123 2017-12 American Pan Pacific 312 124 2017-12 Emirates 280 125 2017-12 Japan Air 309 126 2017-12 KLM 312 127 2017-12 Lufthansa 293 128 2017-12 Scandinavian Air 298 129 2017-12 Singapore Airlines 274 130 2018-12 Air Canada 292 131 2018-12 Air Force 1 261 132 2018-12 American Air 318 133 2018-12 American Pan Pacific 297 134 2018-12 Emirates 312 135 2018-12 Japan Air 297 136 2018-12 KLM 264 137 2018-12 Lufthansa 286 138 2018-12 Scandinavian Air 300 139 2018-12 Singapore Airlines 303 140 2019-12 Air Canada 272 141 2019-12 Air Force 1 306 142 2019-12 American Air 288 143 2019-12 American Pan Pacific 287 144 2019-12 Emirates 281 145 2019-12 Japan Air 310 146 2019-12 KLM 288 147 2019-12 Lufthansa 296 148 2019-12 Scandinavian Air 335 149 2019-12 Singapore Airlines 267 150 2020-12 Air Canada 295 151 2020-12 Air Force 1 306 152 2020-12 American Air 267 153 2020-12 American Pan Pacific 305 154 2020-12 Emirates 294 155 2020-12 Japan Air 251 156 2020-12 KLM 326 157 2020-12 Lufthansa 336 158 2020-12 Scandinavian Air 282 159 2020-12 Singapore Airlines 275 160 2021-12 Air Canada 124 161 2021-12 Air Force 1 132 162 2021-12 American Air 118 163 2021-12 American Pan Pacific 115 164 2021-12 Emirates 134 165 2021-12 Japan Air 110 166 2021-12 KLM 115 167 2021-12 Lufthansa 125 168 2021-12 Scandinavian Air 130 169 2021-12 Singapore Airlines 110dfpOperating_Airlines Air Canada Air Force 1 American Air American Pan Pacific Emirates Japan Air KLM Lufthansa Scandinavian Air Singapore Airlines Dates 2005-12 139 147 138 148 159 136 137 151 166 156 2006-12 277 297 299 286 312 297 312 290 270 290 2007-12 301 268 267 297 298 291 295 290 316 307 2008-12 296 279 286 316 306 290 286 284 281 314 2009-12 312 264 329 276 284 273 313 304 292 283 2010-12 300 279 291 292 284 309 303 283 309 280 2011-12 293 304 279 330 279 287 286 281 299 292 2012-12 312 291 276 312 303 304 271 282 301 286 2013-12 274 301 298 283 347 303 270 290 279 284 2014-12 288 317 312 296 309 275 273 278 296 286 2015-12 257 291 305 279 331 285 320 306 280 276 2016-12 274 292 272 322 309 281 263 305 328 292 2017-12 291 263 298 312 280 309 312 293 298 274 2018-12 292 261 318 297 312 297 264 286 300 303 2019-12 272 306 288 287 281 310 288 296 335 267 2020-12 295 306 267 305 294 251 326 336 282 275 2021-12 124 132 118 115 134 110 115 125 130 110