from purpleair import purpleair
p = purpleair.PurpleAir()  # Initialized 10589 sensors!
print(len(p.useful_sensors))  # List of sensors with no defects

from purpleair import sensor
s = sensor.Sensor('2891', parse_location=True)
print(s)  # Sensor 2891 at 10834, Canyon Road, Omaha, Douglas County, Nebraska, 68112, USA

from purpleair import purpleair
df = p.to_dataframe('all')  # Other options include 'outdoor' and 'useful'

# Will print
          age  downgraded  flagged  hidden  humidity           last_seen  ...                  name   parent  pm_2.5 pressure     temp_c  temp_f
id                                                                        ...
24115   36026       False    False   False      0.15 2019-01-09 20:33:05  ...   2nd South 12th East      NaN    0.15   869.14  31.666667    89.0
16791       0       False    False   False      0.60 2019-02-03 20:59:26  ...                DW0435      NaN    1.96  1009.82  30.000000    86.0
16792       0       False    False   False      0.60 2019-02-03 20:59:29  ...              DW0435 B  16791.0    1.65  1009.77  30.000000    86.0
14633       0       False    False   False      0.55 2019-02-03 20:59:48  ...     Hazelwood canary       NaN    0.29  1000.25  18.333333    65.0
6522   538227       False    False   False      0.22 2018-01-26 02:32:25  ...                Indoor      NaN    1.33   837.97  23.888889    75.0

from purpleair import sensor
se = sensor.Sensor('2891', parse_location=True)
print(se.get_historical(3).head())

# Will print
                        created_at  PM1 CF=ATM ug/m3  PM25 CF=ATM ug/m3  PM10 CF=ATM ug/m3  Free HEAP memory  ADC0 Voltage  Sensor Firmware  Unused  PM25 CF=1 ug/m3
entry_id
536245   2019-01-27 00:00:35+00:00             12.87              17.70              18.39           30032.0          0.02           973.99     NaN            17.70
536246   2019-01-27 00:01:55+00:00             13.04              18.13              18.53           30584.0          0.02           974.00     NaN            18.13
536247   2019-01-27 00:03:15+00:00             14.80              18.91              20.33           30632.0          0.02           973.97     NaN            18.91
536248   2019-01-27 00:04:36+00:00             14.64              19.22              20.98           30720.0          0.02           974.03     NaN            19.22
536249   2019-01-27 00:05:55+00:00             15.16              19.71              20.56           30776.0          0.02           974.05     NaN            19.71