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72 | 72 | "# but that date syntax is kind of ugly, so we have a helper to produce it for you from python dates\n", |
73 | 73 | "import datetime\n", |
74 | 74 | "start_date = datetime.date(2019,1,1)\n", |
75 | | - "end_date = datetime.date(2020,1,1)\n", |
76 | | - "date_range_2019 = mc.publish_date_query(start_date, end_date) # default is start inclusive, end exclusive\n", |
| 75 | + "end_date = datetime.date(2019,12,31)\n", |
| 76 | + "date_range_2019 = mc.dates_as_query_clause(start_date, end_date) # default is start & end inclusive\n", |
77 | 77 | "mc.storyCount(my_query, date_range_2019)" |
78 | 78 | ] |
79 | 79 | }, |
|
240 | 240 | "outputs": [], |
241 | 241 | "source": [ |
242 | 242 | "# let's fetch all the stories matching our query (this can take a few minutes)\n", |
243 | | - "jan_2020 = mc.publish_date_query(datetime.date(2020,1,1), datetime.date(2020,2,1))\n", |
| 243 | + "jan_2020 = mc.dates_as_query_clause(datetime.date(2020,1,1), datetime.date(2020,1,31))\n", |
244 | 244 | "all_stories = all_matching_stories(mc, us_query, jan_2020)\n", |
245 | 245 | "len(all_stories)" |
246 | 246 | ] |
|
293 | 293 | "import pandas\n", |
294 | 294 | "pandas.read_csv('story-list.csv')" |
295 | 295 | ] |
| 296 | + }, |
| 297 | + { |
| 298 | + "cell_type": "code", |
| 299 | + "execution_count": null, |
| 300 | + "metadata": {}, |
| 301 | + "outputs": [], |
| 302 | + "source": [] |
296 | 303 | } |
297 | 304 | ], |
298 | 305 | "metadata": { |
|
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