部署机器学习模型#
需要在新数据上重复特征工程步骤。在某些情况下，这些步骤需要在接近实时的情况下执行。Featuretools具有简化特征工程部署的功能。
保存特征#
首先，让我们生成一些以相同格式的训练和测试数据。我们使用随机种子生成不同的测试数据。
Note
Features saved in one version of Featuretools are not guaranteed to load in another. This means the features might need to be re-created after upgrading Featuretools.
[1]:
import featuretools as ft
es_train = ft.demo.load_mock_customer(return_entityset=True)
es_test = ft.demo.load_mock_customer(return_entityset=True, random_seed=33)
2024-10-11 14:49:33,066 featuretools - WARNING While loading primitives via "premium_primitives" entry point, ignored primitive "DiversityScore" from "premium_primitives.diversity_score" because a primitive with that name already exists in "nlp_primitives.diversity_score"
2024-10-11 14:49:33,067 featuretools - WARNING While loading primitives via "premium_primitives" entry point, ignored primitive "LSA" from "premium_primitives.lsa" because a primitive with that name already exists in "nlp_primitives.lsa"
2024-10-11 14:49:33,067 featuretools - WARNING While loading primitives via "premium_primitives" entry point, ignored primitive "MeanCharactersPerSentence" from "premium_primitives.mean_characters_per_sentence" because a primitive with that name already exists in "nlp_primitives.mean_characters_per_sentence"
2024-10-11 14:49:33,067 featuretools - WARNING While loading primitives via "premium_primitives" entry point, ignored primitive "NumberOfSentences" from "premium_primitives.number_of_sentences" because a primitive with that name already exists in "nlp_primitives.number_of_sentences"
2024-10-11 14:49:33,067 featuretools - WARNING While loading primitives via "premium_primitives" entry point, ignored primitive "PartOfSpeechCount" from "premium_primitives.part_of_speech_count" because a primitive with that name already exists in "nlp_primitives.part_of_speech_count"
2024-10-11 14:49:33,068 featuretools - WARNING While loading primitives via "premium_primitives" entry point, ignored primitive "PolarityScore" from "premium_primitives.polarity_score" because a primitive with that name already exists in "nlp_primitives.polarity_score"
2024-10-11 14:49:33,068 featuretools - WARNING While loading primitives via "premium_primitives" entry point, ignored primitive "StopwordCount" from "premium_primitives.stopword_count" because a primitive with that name already exists in "nlp_primitives.stopword_count"
2024-10-11 14:49:33,086 featuretools - WARNING Featuretools failed to load plugin tsfresh from library featuretools_tsfresh_primitives.__init__. For a full stack trace, set logging to debug.
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
/Users/code/fin_tool/github/featuretools/venv/lib/python3.11/site-packages/woodwork/type_sys/utils.py:40: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
pd.to_datetime(
现在让我们使用深度优先搜索（DFS）构建一些特征定义。由于我们有分类特征，我们还会根据训练数据中的数值使用独热编码对它们进行编码。
[2]:
feature_matrix, feature_defs = ft.dfs(
entityset=es_train, target_dataframe_name="customers"
)
feature_matrix_enc, features_enc = ft.encode_features(feature_matrix, feature_defs)
feature_matrix_enc
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x106a580e0> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x106a58ae0> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x106a589a0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x106a53f60> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x106a53880> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x106a58ae0> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x106a53f60> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x106a580e0> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x106a589a0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x106a53880> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x106a53880> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x106a580e0> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x106a589a0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x106a53f60> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x106a58ae0> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x106a589a0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x106a58ae0> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x106a53f60> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x106a53880> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x106a589a0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x106a58ae0> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x106a580e0> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x106a53880> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x106a53f60> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
[2]:
| COUNT(sessions) | NUM_UNIQUE(sessions.device) | COUNT(transactions) | MAX(transactions.amount) | MEAN(transactions.amount) | MIN(transactions.amount) | NUM_UNIQUE(transactions.product_id) | SKEW(transactions.amount) | STD(transactions.amount) | SUM(transactions.amount) | ... | MODE(sessions.MODE(transactions.product_id)) is unknown | MODE(sessions.MONTH(session_start)) = 1 | MODE(sessions.MONTH(session_start)) is unknown | MODE(sessions.WEEKDAY(session_start)) = 2 | MODE(sessions.WEEKDAY(session_start)) is unknown | MODE(sessions.YEAR(session_start)) = 2014 | MODE(sessions.YEAR(session_start)) is unknown | MODE(transactions.sessions.device) = mobile | MODE(transactions.sessions.device) = desktop | MODE(transactions.sessions.device) is unknown | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| customer_id | |||||||||||||||||||||
| 5 | 6 | 3 | 79 | 149.02 | 80.375443 | 7.55 | 5 | -0.025941 | 44.095630 | 6349.66 | ... | False | True | False | True | False | True | False | True | False | False |
| 4 | 8 | 3 | 109 | 149.95 | 80.070459 | 5.73 | 5 | -0.036348 | 45.068765 | 8727.68 | ... | False | True | False | True | False | True | False | True | False | False |
| 1 | 8 | 3 | 126 | 139.43 | 71.631905 | 5.81 | 5 | 0.019698 | 40.442059 | 9025.62 | ... | False | True | False | True | False | True | False | True | False | False |
| 3 | 6 | 3 | 93 | 149.15 | 67.060430 | 5.89 | 5 | 0.418230 | 43.683296 | 6236.62 | ... | False | True | False | True | False | True | False | False | True | False |
| 2 | 7 | 3 | 93 | 146.81 | 77.422366 | 8.73 | 5 | 0.098259 | 37.705178 | 7200.28 | ... | False | True | False | True | False | True | False | False | True | False |
5 rows × 121 columns
现在，我们可以使用featuretools.save_features将特征列表保存到一个json文件中。
[3]:
ft.save_features(features_enc, "feature_definitions.json")
计算新数据的特征矩阵#
我们可以使用featuretools.load_features来读取一个保存的特征列表，以便计算我们的新实体集的特征。
[4]:
saved_features = ft.load_features("feature_definitions.json")
加载特征后，我们可以计算特征矩阵。
[5]:
feature_matrix = ft.calculate_feature_matrix(saved_features, es_test)
feature_matrix
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x106a580e0> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x106a58ae0> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x106a589a0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x106a53f60> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x106a53880> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x106a58ae0> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x106a53f60> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x106a580e0> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x106a589a0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x106a53880> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function mean at 0x106a589a0> is currently using SeriesGroupBy.mean. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "mean" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function std at 0x106a58ae0> is currently using SeriesGroupBy.std. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "std" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function min at 0x106a580e0> is currently using SeriesGroupBy.min. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "min" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function sum at 0x106a53880> is currently using SeriesGroupBy.sum. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "sum" instead.
).agg(to_agg)
/Users/code/fin_tool/github/featuretools/featuretools/computational_backends/feature_set_calculator.py:756: FutureWarning: The provided callable <function max at 0x106a53f60> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string "max" instead.
).agg(to_agg)
[5]:
| zip_code = 60091 | zip_code = 13244 | zip_code is unknown | COUNT(sessions) | MODE(sessions.device) = mobile | MODE(sessions.device) = desktop | MODE(sessions.device) is unknown | NUM_UNIQUE(sessions.device) | COUNT(transactions) | MAX(transactions.amount) | ... | SUM(sessions.MAX(transactions.amount)) | SUM(sessions.MEAN(transactions.amount)) | SUM(sessions.MIN(transactions.amount)) | SUM(sessions.NUM_UNIQUE(transactions.product_id)) | SUM(sessions.SKEW(transactions.amount)) | SUM(sessions.STD(transactions.amount)) | MODE(transactions.sessions.device) = mobile | MODE(transactions.sessions.device) = desktop | MODE(transactions.sessions.device) is unknown | NUM_UNIQUE(transactions.sessions.device) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| customer_id | |||||||||||||||||||||
| 1 | True | False | False | 6 | False | True | False | 3 | 73 | 147.64 | ... | 834.08 | 524.919674 | 198.92 | 25.0 | -1.546156 | 217.064024 | True | False | False | 3 |
| 4 | False | True | False | 9 | False | True | False | 3 | 126 | 147.55 | ... | 1180.90 | 733.862898 | 193.08 | 43.0 | -1.797214 | 319.497611 | False | True | False | 3 |
| 3 | True | False | False | 5 | True | False | False | 2 | 64 | 148.09 | ... | 715.80 | 407.390549 | 108.69 | 23.0 | 0.353061 | 215.417211 | True | False | False | 2 |
| 2 | False | True | False | 8 | False | True | False | 3 | 129 | 148.34 | ... | 1100.82 | 615.714934 | 136.01 | 39.0 | -0.082021 | 315.817331 | False | True | False | 3 |
| 5 | True | False | False | 7 | False | True | False | 3 | 108 | 149.53 | ... | 997.48 | 584.302915 | 137.50 | 33.0 | -0.595128 | 261.535265 | False | True | False | 3 |
5 rows × 121 columns
正如您在上面所看到的，我们拥有与之前完全相同的特征，但是是使用测试数据计算得出的。
导出特征矩阵#
另存为csv#
特征矩阵是一个可以保存到磁盘的 pandas DataFrame。
[6]:
feature_matrix.to_csv("feature_matrix.csv")
我们也可以按照以下方式将其读取回来：
[7]:
import pandas as pd
saved_fm = pd.read_csv("feature_matrix.csv", index_col="customer_id")
saved_fm
[7]:
| zip_code = 60091 | zip_code = 13244 | zip_code is unknown | COUNT(sessions) | MODE(sessions.device) = mobile | MODE(sessions.device) = desktop | MODE(sessions.device) is unknown | NUM_UNIQUE(sessions.device) | COUNT(transactions) | MAX(transactions.amount) | ... | SUM(sessions.MAX(transactions.amount)) | SUM(sessions.MEAN(transactions.amount)) | SUM(sessions.MIN(transactions.amount)) | SUM(sessions.NUM_UNIQUE(transactions.product_id)) | SUM(sessions.SKEW(transactions.amount)) | SUM(sessions.STD(transactions.amount)) | MODE(transactions.sessions.device) = mobile | MODE(transactions.sessions.device) = desktop | MODE(transactions.sessions.device) is unknown | NUM_UNIQUE(transactions.sessions.device) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| customer_id | |||||||||||||||||||||
| 1 | True | False | False | 6 | False | True | False | 3 | 73 | 147.64 | ... | 834.08 | 524.919674 | 198.92 | 25.0 | -1.546156 | 217.064024 | True | False | False | 3 |
| 4 | False | True | False | 9 | False | True | False | 3 | 126 | 147.55 | ... | 1180.90 | 733.862898 | 193.08 | 43.0 | -1.797214 | 319.497611 | False | True | False | 3 |
| 3 | True | False | False | 5 | True | False | False | 2 | 64 | 148.09 | ... | 715.80 | 407.390549 | 108.69 | 23.0 | 0.353061 | 215.417211 | True | False | False | 2 |
| 2 | False | True | False | 8 | False | True | False | 3 | 129 | 148.34 | ... | 1100.82 | 615.714934 | 136.01 | 39.0 | -0.082021 | 315.817331 | False | True | False | 3 |
| 5 | True | False | False | 7 | False | True | False | 3 | 108 | 149.53 | ... | 997.48 | 584.302915 | 137.50 | 33.0 | -0.595128 | 261.535265 | False | True | False | 3 |
5 rows × 121 columns