{"id":199100,"date":"2023-11-20T10:04:40","date_gmt":"2023-11-20T15:04:40","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=199100"},"modified":"2023-11-20T10:05:11","modified_gmt":"2023-11-20T15:05:11","slug":"employing-human-order-in-pandas-dataframe-sorting-risk-factors-and-tenors","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/employing-human-order-in-pandas-dataframe-sorting-risk-factors-and-tenors\/","title":{"rendered":"Employing Human Order in pandas DataFrame Sorting: Risk Factors and Tenors"},"content":{"rendered":"\n

The post “Employing Human Order in pandas DataFrame Sorting: Risk Factors and Tenors” first appeared on Quant at Risk<\/a> blog.<\/em><\/p>\n\n\n\n

There are various Python projects which require sorting but not the ones that employ a default alphanumeric functionality. We talk about manually specified order or human-order<\/em>, in short. One of such examples is the case study presented below.<\/p>\n\n\n\n

Imagine that your risk system provides you with a list of various risk factors and the corresponding risk tenors. The latter may differ among risk factors and your task is to quickly convert an input data row into a matrix of tenor-series assuming that all your tenors follow a human-order defined as:<\/p>\n\n\n\n

1W, 2W, 3W, 1M, 2M, 3M, \u2026, 11M, 1Y, 2Y, 3Y, \u2026, 49Y, 50Y<\/p>\n\n\n\n

Let\u2019s analyse an exemplary input dataset:<\/p>\n\n\n\n

# Employing Human-Order in pandas DataFrame Sorting: Risk Factors and Tenors\n# (c) 2019 by QuantAtRisk.com\n \nimport numpy as np\nimport pandas as pd\n \ndata = pd.read_csv('Libo3.csv', header=0)\ndisplay(data)<\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
The trouble with standard sorting order is that usually it will end up with 10Y placed before 1Y or week-tenors (W) will follow monthly tenors since M appears before W, and\u2026 we don\u2019t want the outcome to be that way. We can see that if we attempt to perform:<\/p>\n\n\n\n
for risk_factor in data.Risk_Factor.unique():\n    print(risk_factor)\n    lst = data[data.Risk_Factor == risk_factor].Tenor.tolist()\n    print(sorted(lst))\n    print()<\/pre>\n\n\n\n
Libo3.GBP.ON.SWAP
[’10M’, ‘1M’, ‘1W’, ‘2Y’, ’30Y’, ‘3W’, ‘5M’, ‘7M’]<\/p>\n\n\n\n
Libo3.EUR.ON.SWAP
[’10M’, ‘1M’, ‘1W’, ’21Y’, ’30Y’, ‘3W’, ‘4M’, ‘7M’]<\/p>\n\n\n\n
Solution<\/strong><\/p>\n\n\n\n
First we need to specify a human-order sorting key:<\/p>\n\n\n\n
ho = ['ON']  # overnight\nho.extend([str(i) + 'W' for i in range(1,4)])  # weekly tenors\nho.extend([str(i) + 'M' for i in range(1,12)])  # monthly tenors\nho.extend([str(i) + 'Y' for i in range(1,51)])  # yearly tenors<\/pre>\n\n\n\n
what will store:<\/p>\n\n\n\n
print(ho)<\/pre>\n\n\n\n
[‘ON’, ‘1W’, ‘2W’, ‘3W’, ‘1M’, ‘2M’, ‘3M’, ‘4M’, ‘5M’, ‘6M’, ‘7M’, ‘8M’, ‘9M’, ’10M’,
’11M’, ‘1Y’, ‘2Y’, ‘3Y’, ‘4Y’, ‘5Y’, ‘6Y’, ‘7Y’, ‘8Y’, ‘9Y’, ’10Y’, ’11Y’, ’12Y’,
’13Y’, ’14Y’, ’15Y’, ’16Y’, ’17Y’, ’18Y’, ’19Y’, ’20Y’, ’21Y’, ’22Y’, ’23Y’, ’24Y’,
’25Y’, ’26Y’, ’27Y’, ’28Y’, ’29Y’, ’30Y’, ’31Y’, ’32Y’, ’33Y’, ’34Y’, ’35Y’, ’36Y’,
’37Y’, ’38Y’, ’39Y’, ’40Y’, ’41Y’, ’42Y’, ’43Y’, ’44Y’, ’45Y’, ’46Y’, ’47Y’, ’48Y’,
’49Y’, ’50Y’]<\/p>\n\n\n\n
Next, we need to set Tenor<\/em>s as \u201ccategory\u201d type (line #23-24) and sort values according to requested order (line #25). Have a look:<\/p>\n\n\n\n
data.Tenor = data.Tenor.astype('category')\ndata.Tenor.cat.set_categories(ho, inplace=True)\ndata = data.sort_values([\"Risk_Factor\", \"Tenor\"])\ndata.reset_index(inplace=True, drop=True)\n \ndisplay(data)  # let's inspect the result<\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
That accomplishes our task. From now on, you can manipulate the data in any way, for example:<\/p>\n\n\n\n
fi = True  # first iteration\n \nfor risk_factor in data.Risk_Factor.unique():\n    tmp = data[data.Risk_Factor == risk_factor]\n    tmp.set_index('Tenor', drop=True, inplace=True)\n    tmp = tmp.rename(columns={tmp.columns[1] : risk_factor})\n    del tmp[tmp.columns[0]]\n    if fi:\n        df = tmp\n        fi = False\n    else:\n        df = df.join(tmp, how='outer', sort=True)  # applying 'sort' keyword\n                                                   # here preserves a correct \n                                                   # human-order\ndisplay(df)<\/pre>\n\n\n\n
will allow you for every risk factor to join the data using all available Tenor<\/em>s set as a common index:<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
Disclaimer: The input data were randomly generated.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"
Imagine that your risk system provides you with a list of various risk factors and the corresponding risk tenors. <\/p>\n","protected":false},"author":710,"featured_media":36684,"comment_status":"open","ping_status":"closed","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,349,338,341],"tags":[806,4922,1225,1224,595],"contributors-categories":[13732],"class_list":{"0":"post-199100","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-python-development","10":"category-ibkr-quant-news","11":"category-quant-development","12":"tag-data-science","13":"tag-econometrics","14":"tag-numpy","15":"tag-pandas","16":"tag-python","17":"contributors-categories-quant-at-risk"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nEmploying Human 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