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Update README.md with DataFrame example
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@hedgertronic
hedgertronic committed Oct 12, 2022
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Expand Up @@ -21,6 +21,7 @@ WHOOP API documentation can be found at https://developer.whoop.com/api.
- [Get Sleep Collection](#get-sleep-collection)
- [Get Workout By ID](#get-workout-by-id)
- [Get Workout Collection](#get-workout-collection)
- [Usage With DataFrame](#usage-with-dataframe)

## Installation

Expand Down Expand Up @@ -399,3 +400,166 @@ Get all workouts for a user. Results are sorted by start time in descending orde
...
]
```

## Usage With DataFrame

Using WHOOP API data with a Pandas DataFrame is very straightforward:

```python
>>> import pandas as pd

>>> sleep = client.get_sleep_collection("2022-05-01", "2022-05-07")
>>> pd.json_normalize(sleep)

id user_id created_at updated_at \
0 430878903 995945 2022-05-07T14:56:28.389Z 2022-05-07T15:12:22.933Z
1 430378149 995945 2022-05-06T18:11:27.029Z 2022-05-06T18:11:29.172Z
2 429704502 995945 2022-05-05T14:31:14.954Z 2022-05-05T14:43:15.744Z
3 429055399 995945 2022-05-04T13:35:13.911Z 2022-05-04T13:35:15.758Z
4 428375477 995945 2022-05-03T12:26:02.170Z 2022-05-03T12:26:04.151Z
5 427873268 995945 2022-05-02T15:55:10.734Z 2022-05-02T15:55:13.140Z
6 427300091 995945 2022-05-01T17:06:54.808Z 2022-05-01T17:06:57.067Z
7 427069852 995945 2022-05-01T11:26:47.991Z 2022-05-01T11:26:49.684Z

start end timezone_offset nap \
0 2022-05-07T04:46:52.867Z 2022-05-07T14:40:57.427Z -04:00 False
1 2022-05-06T05:09:00.681Z 2022-05-06T15:09:12.415Z -04:00 False
2 2022-05-05T04:59:16.774Z 2022-05-05T14:29:44.886Z -04:00 False
3 2022-05-04T05:04:02.916Z 2022-05-04T13:28:57.733Z -04:00 False
4 2022-05-03T05:29:46.133Z 2022-05-03T12:14:54.861Z -04:00 False
5 2022-05-02T05:12:48.073Z 2022-05-02T15:49:36.453Z -04:00 False
6 2022-05-01T11:53:13.942Z 2022-05-01T14:51:19.894Z -04:00 True
7 2022-05-01T06:14:52.087Z 2022-05-01T09:19:14.856Z -04:00 False

score_state score.stage_summary.total_in_bed_time_milli \
0 SCORED 35643599
1 SCORED 35460907
2 SCORED 34240959
3 SCORED 30293855
4 SCORED 24307767
5 SCORED 38003623
6 SCORED 10396599
7 SCORED 11004129

score.stage_summary.total_awake_time_milli \
0 3744035
1 2064515
2 2937299
3 2769352
4 3384733
5 2235345
6 681103
7 1608235

score.stage_summary.total_no_data_time_milli \
0 0
1 0
2 0
3 0
4 0
5 0
6 0
7 0

score.stage_summary.total_light_sleep_time_milli \
0 15384266
1 13169346
2 12210534
3 12689932
4 6655383
5 11195003
6 5900698
7 4971785

score.stage_summary.total_slow_wave_sleep_time_milli \
0 7633425
1 7874053
2 7814364
3 5554196
4 5526290
5 10101551
6 1762533
7 3008130

score.stage_summary.total_rem_sleep_time_milli \
0 8881873
1 12352993
2 11278762
3 9280375
4 8741361
5 14471724
6 2052265
7 1415979

score.stage_summary.sleep_cycle_count \
0 8
1 9
2 5
3 4
4 4
5 9
6 3
7 1

score.stage_summary.disturbance_count score.sleep_needed.baseline_milli \
0 17 27975800
1 12 27976121
2 21 27976442
3 15 27976764
4 12 27977085
5 8 27977406
6 1 27977728
7 4 27977728

score.sleep_needed.need_from_sleep_debt_milli \
0 0
1 359692
2 3237274
3 3636979
4 0
5 9792092
6 9792204
7 2098329

score.sleep_needed.need_from_recent_strain_milli \
0 367539
1 304726
2 655901
3 1812597
4 270597
5 426795
6 0
7 1463907

score.sleep_needed.need_from_recent_nap_milli score.respiratory_rate \
0 0 13.398438
1 0 13.066406
2 0 12.568359
3 0 13.164062
4 0 13.535156
5 -9715496 13.535156
6 0 14.121094
7 0 14.941406

score.sleep_performance_percentage score.sleep_consistency_percentage \
0 100.0 83.0
1 100.0 81.0
2 98.0 75.0
3 82.0 68.0
4 74.0 65.0
5 100.0 62.0
6 26.0 51.0
7 30.0 78.0

score.sleep_efficiency_percentage
0 89.857010
1 94.178050
2 91.421684
3 91.993940
4 89.586520
5 94.118080
6 93.448790
7 87.927660

[8 rows x 25 columns]
```

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