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Update README with supported versions, pandas v1 outputs

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Seth Michael Larson 2020-03-27 13:13:50 -05:00 committed by GitHub
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@ -53,29 +53,30 @@ index pattern, and explore using an API that mirrors a subset of the pandas.Data
>>> import eland as ed >>> import eland as ed
>>> # Connect to 'flights' index via localhost Elasticsearch node >>> # Connect to 'flights' index via localhost Elasticsearch node
>>> df = ed.DataFrame('localhost:9200', 'flights') >>> df = ed.DataFrame('localhost:9200', 'flights')
>>> df.head() >>> df.head()
AvgTicketPrice Cancelled Carrier ... OriginWeather dayOfWeek timestamp AvgTicketPrice Cancelled ... dayOfWeek timestamp
0 841.265642 False Kibana Airlines ... Sunny 0 2018-01-01 00:00:00 0 841.265642 False ... 0 2018-01-01 00:00:00
1 882.982662 False Logstash Airways ... Clear 0 2018-01-01 18:27:00 1 882.982662 False ... 0 2018-01-01 18:27:00
2 190.636904 False Logstash Airways ... Rain 0 2018-01-01 17:11:14 2 190.636904 False ... 0 2018-01-01 17:11:14
3 181.694216 True Kibana Airlines ... Thunder & Lightning 0 2018-01-01 10:33:28 3 181.694216 True ... 0 2018-01-01 10:33:28
4 730.041778 False Kibana Airlines ... Damaging Wind 0 2018-01-01 05:13:00 4 730.041778 False ... 0 2018-01-01 05:13:00
[5 rows x 27 columns] [5 rows x 27 columns]
>>> df.describe() >>> df.describe()
AvgTicketPrice DistanceKilometers DistanceMiles FlightDelayMin FlightTimeHour FlightTimeMin dayOfWeek AvgTicketPrice DistanceKilometers ... FlightTimeMin dayOfWeek
count 13059.000000 13059.000000 13059.000000 13059.000000 13059.000000 13059.000000 13059.000000 count 13059.000000 13059.000000 ... 13059.000000 13059.000000
mean 628.253689 7092.142457 4406.853010 47.335171 8.518797 511.127842 2.835975 mean 628.253689 7092.142457 ... 511.127842 2.835975
std 266.386661 4578.263193 2844.800855 96.743006 5.579019 334.741135 1.939365 std 266.386661 4578.263193 ... 334.741135 1.939365
min 100.020531 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 min 100.020531 0.000000 ... 0.000000 0.000000
25% 410.008918 2470.545974 1535.126118 0.000000 4.194976 251.738513 1.000000 25% 410.008918 2470.545974 ... 251.739008 1.000000
50% 640.362667 7612.072403 4729.922470 0.000000 8.385816 503.148975 3.000000 50% 640.387285 7612.072403 ... 503.148975 3.000000
75% 842.254990 9735.082407 6049.459005 15.000000 12.009396 720.534532 4.141221 75% 842.262193 9735.660463 ... 720.505705 4.239865
max 1199.729004 19881.482422 12353.780273 360.000000 31.715034 1902.901978 6.000000 max 1199.729004 19881.482422 ... 1902.901978 6.000000
[8 rows x 7 columns]
>>> df[['Carrier', 'AvgTicketPrice', 'Cancelled']] >>> df[['Carrier', 'AvgTicketPrice', 'Cancelled']]
Carrier AvgTicketPrice Cancelled Carrier AvgTicketPrice Cancelled
0 Kibana Airlines 841.265642 False 0 Kibana Airlines 841.265642 False
@ -93,12 +94,12 @@ max 1199.729004 19881.482422 12353.780273 360.000000 3
[13059 rows x 3 columns] [13059 rows x 3 columns]
>>> df[(df.Carrier=="Kibana Airlines") & (df.AvgTicketPrice > 900.0) & (df.Cancelled == True)].head() >>> df[(df.Carrier=="Kibana Airlines") & (df.AvgTicketPrice > 900.0) & (df.Cancelled == True)].head()
AvgTicketPrice Cancelled Carrier ... OriginWeather dayOfWeek timestamp AvgTicketPrice Cancelled ... dayOfWeek timestamp
8 960.869736 True Kibana Airlines ... Heavy Fog 0 2018-01-01 12:09:35 8 960.869736 True ... 0 2018-01-01 12:09:35
26 975.812632 True Kibana Airlines ... Rain 0 2018-01-01 15:38:32 26 975.812632 True ... 0 2018-01-01 15:38:32
311 946.358410 True Kibana Airlines ... Heavy Fog 0 2018-01-01 11:51:12 311 946.358410 True ... 0 2018-01-01 11:51:12
651 975.383864 True Kibana Airlines ... Rain 2 2018-01-03 21:13:17 651 975.383864 True ... 2 2018-01-03 21:13:17
950 907.836523 True Kibana Airlines ... Thunder & Lightning 2 2018-01-03 05:14:51 950 907.836523 True ... 2 2018-01-03 05:14:51
[5 rows x 27 columns] [5 rows x 27 columns]
@ -128,7 +129,6 @@ dtype: int64
13057 20819.488281 13057 20819.488281
13058 18315.431274 13058 18315.431274
Length: 13059, dtype: float64 Length: 13059, dtype: float64
>>> print(s.info_es()) >>> print(s.info_es())
index_pattern: flights index_pattern: flights
Index: Index:
@ -148,12 +148,12 @@ Operations:
>>> pd_df = ed.eland_to_pandas(df) >>> pd_df = ed.eland_to_pandas(df)
>>> pd_df.head() >>> pd_df.head()
AvgTicketPrice Cancelled Carrier ... OriginWeather dayOfWeek timestamp AvgTicketPrice Cancelled ... dayOfWeek timestamp
0 841.265642 False Kibana Airlines ... Sunny 0 2018-01-01 00:00:00 0 841.265642 False ... 0 2018-01-01 00:00:00
1 882.982662 False Logstash Airways ... Clear 0 2018-01-01 18:27:00 1 882.982662 False ... 0 2018-01-01 18:27:00
2 190.636904 False Logstash Airways ... Rain 0 2018-01-01 17:11:14 2 190.636904 False ... 0 2018-01-01 17:11:14
3 181.694216 True Kibana Airlines ... Thunder & Lightning 0 2018-01-01 10:33:28 3 181.694216 True ... 0 2018-01-01 10:33:28
4 730.041778 False Kibana Airlines ... Damaging Wind 0 2018-01-01 05:13:00 4 730.041778 False ... 0 2018-01-01 05:13:00
[5 rows x 27 columns] [5 rows x 27 columns]
``` ```
@ -168,16 +168,16 @@ Binary installers for the latest released version are available at the [Python
package index](https://pypi.org/project/eland). package index](https://pypi.org/project/eland).
```sh ```sh
pip install eland python -m pip install eland
``` ```
## Versions and Compatibility ## Versions and Compatibility
### Python Version Support ### Python Version Support
Officially Python 3.5.3 and above, 3.6, 3.7, and 3.8. Officially Python 3.6 and above.
eland depends on pandas version 0.25.3. eland depends on pandas version 1.0.0+.
### Elasticsearch Versions ### Elasticsearch Versions