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52 | 52 | "from calitp_data_analysis.tables import tbls\n",
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53 | 53 | "from siuba import *\n",
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54 | 54 | "\n",
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55 |
| - "from calitp_data_analysis import geography_utils\n", |
56 |
| - "from shared_utils import geography_utils, utils" |
| 55 | + "from calitp_data_analysis import geography_utils, utils\n", |
| 56 | + "from shared_utils import portfolio_utils" |
57 | 57 | ]
|
58 | 58 | },
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59 | 59 | {
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|
424 | 424 | "count_cols = [\"pickup\"]\n",
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425 | 425 | "nunique_cols = [\"pickup_zone\"]\n",
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426 | 426 | "\n",
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427 |
| - "by_borough = geography_utils.aggregate_by_geography(\n", |
| 427 | + "by_borough = portfolio_utils.aggregate_by_geography(\n", |
428 | 428 | " df[df.pickup_borough.notna()], \n",
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429 | 429 | " group_cols=group_cols,\n",
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430 | 430 | " sum_cols = sum_cols,\n",
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|
749 | 749 | }
|
750 | 750 | ],
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751 | 751 | "source": [
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752 |
| - "df2 = geography_utils.aggregate_by_geography(\n", |
| 752 | + "df2 = portfolio_utils.aggregate_by_geography(\n", |
753 | 753 | " df[(df.payment.notna()) & (df.pickup_borough.notna())], \n",
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754 | 754 | " group_cols = [\"pickup_borough\", \"payment\"],\n",
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755 | 755 | " sum_cols = [\"passengers\", \"fare\"],\n",
|
|
981 | 981 | "source": [
|
982 | 982 | "import branca\n",
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983 | 983 | "import geopandas as gpd\n",
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984 |
| - "import pandas as pd\n", |
985 |
| - "\n", |
986 |
| - "from calitp_data_analysis import geography_utils" |
| 984 | + "import pandas as pd" |
987 | 985 | ]
|
988 | 986 | },
|
989 | 987 | {
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