
import pandas as pd
from plotnine import *
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import display, Markdown


airdata = pd.read_pickle("data/airdata/air.pickle")
airdata


#airdata["minute"] = [dt.minute for dt in airdata.datetime]
#airdata["second"] = [dt.second for dt in airdata.datetime]
airdata["datetime-1"] = airdata["datetime"].shift(1)
airdata["delta"] = airdata["datetime"] - airdata["datetime-1"]
airdata["delta"] = airdata["delta"].dt.seconds
airdata["imputated"] = False
# descartamos las primeras lecturas que tuvieron ciertos 
# detalles de faltantes por reinicios inesperados. 
# afinamos el software y no son 6 observaciones a
# descartar
airdata = airdata.iloc[6:].reset_index(drop=True)

# eliminamos las que no se pudieron obtener lags
#airdata.drop(airdata.head(1).index,inplace=True)
#airdata.drop(airdata.tail(1).index,inplace=True)

airdata.head(10)


(
    ggplot(airdata) +
    geom_histogram(aes(x="delta"), bins=20)
)

<ggplot: (8729120807801)>


#range(airdata[airdata["delta"] != 3].min(), airdata[airdata["delta"] != 3].max(), 1)
display(Markdown("Time difference between readings:"))
display(Markdown(f"* Mín: {airdata['delta'].min()} seconds."))
display(Markdown(f"* Max: {airdata['delta'].max()} seconds."))


airdata[airdata["delta"] > 3]


(
    ggplot(airdata[airdata["delta"] > 3], 
           aes(x="delta")) +
    geom_histogram(bins=10) #+
    #scale_x_discrete(labels=scale, name="delta")
)

<ggplot: (8729118032617)>


airdata.describe().round(2)


airdata[airdata.datetime == "2021-02-18 23:30:02.871531487"].round(2)


airdata[(airdata.datetime >= "2021-02-18 23:22")][((airdata.datetime <= "2021-02-18 23:30:03"))]

/home/jaa6766/.conda/envs/cuda/lib/python3.7/site-packages/ipykernel_launcher.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.


airdata[((airdata.datetime <= "2021-02-18 23:30:03"))].iloc[-15:]


airdata[((airdata.datetime >= "2021-02-18 23:30:02"))].iloc[:15]


(
    ggplot(airdata[(airdata.datetime >= "2021-02-18 23:10")][((airdata.datetime <= "2021-02-18 23:35"))]) +
    geom_point(aes(x="datetime", y="temperature")) +
    theme(axis_text_x=element_text(angle=45)) 
)

/home/jaa6766/.conda/envs/cuda/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.

<ggplot: (8729117976041)>


(
    ggplot(airdata[(airdata.datetime >= "2021-02-18 23:10")][((airdata.datetime <= "2021-02-18 23:35"))]) +
    geom_point(aes(x="datetime", y="pressure")) +
    theme(axis_text_x=element_text(angle=45)) 
)

/home/jaa6766/.conda/envs/cuda/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.

<ggplot: (8729117979797)>


(
    ggplot(airdata[(airdata.datetime >= "2021-02-18 23:10")][((airdata.datetime <= "2021-02-18 23:35"))]) +
    geom_point(aes(x="datetime", y="humidity")) +
    theme(axis_text_x=element_text(angle=45)) 
)

/home/jaa6766/.conda/envs/cuda/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.

<ggplot: (8729117912305)>


(
    ggplot(airdata[(airdata.datetime >= "2021-02-18 23:10")][((airdata.datetime <= "2021-02-18 23:35"))]) +
    geom_point(aes(x="datetime", y="gasResistance")) +
    theme(axis_text_x=element_text(angle=45)) 
)

/home/jaa6766/.conda/envs/cuda/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.

<ggplot: (8729117975997)>


(
    ggplot(airdata[(airdata.datetime >= "2021-02-18 23:10")][((airdata.datetime <= "2021-02-18 23:35"))]) +
    geom_point(aes(x="datetime", y="IAQ")) +
    theme(axis_text_x=element_text(angle=45)) 
)

/home/jaa6766/.conda/envs/cuda/lib/python3.7/site-packages/ipykernel_launcher.py:3: UserWarning: Boolean Series key will be reindexed to match DataFrame index.

<ggplot: (8729118601317)>


Markdown(f"One option could be to discard previous data and only having \
         {airdata[airdata.datetime >= '2021-02-18 23:35:02.871531487'].shape[0]:3,} \
         records out of ({airdata.shape[0]:3,}).")


%%time

def interpolate_missing(df, idx, seconds=3):
    """
    Function to interpolate missing.
    
    examples 
        airdata2 = interpolate_missing(airdata, idx = airdata[airdata.delta > 100].index[0])
        airdata2 = interpolate_missing(airdata, 194044)
    """
    np.random.seed(175904)
    #idx = df[df.delta > 100].index[0]
    #df_prev = df.iloc[idx-100:idx].reset_index(drop=True)
    df_prev = df.loc[idx-int(df.loc[idx]["delta"]):idx].reset_index(drop=True)
    #df_after = df.loc[idx:idx+100].reset_index()
    #display(df_prev)
    
    a  = df_prev.iloc[-2]
    b  = df_prev.iloc[-1]
    
    offset3s = pd.offsets.Second(seconds) # remove closed form
    offset2s = pd.offsets.Second(seconds-2) # remove closed form
    out = {}
    out["datetime"] = pd.date_range(a["datetime"] + offset3s, 
                             b["datetime"] - offset2s, 
                             freq='3s', closed='left')
    out["datetime"] = out["datetime"].set_names("datetime")
    
    for v in ["temperature", "pressure", "humidity", "gasResistance", "IAQ"]:
        i = [i+1 for i, d in enumerate(out["datetime"])]
        m = (b[v] - a[v])/len(out["datetime"])
        sd = 0.7*np.std(df_prev[v])
        rnds = np.random.normal(-sd, sd, len(out["datetime"]))
        #rnds = np.random.uniform(-2*np.pi, 2*np.pi, len(out["datetime"]))
        #rnds = np.cos(rnds) * sd
        out[v] = [m*j + a[v] + rnds[j-1] for j in i]    
        #out[v] = [m*j  b[v] + rnds[j-1] for j in i]
        
    out["iaqAccuracy"] = 1
    
    idf = pd.DataFrame(out)
    reorder_columns = [col for col in out.keys() if col != 'datetime']
    reorder_columns.append("datetime")
    idf = idf.reindex(columns=reorder_columns)
    #print(reorder_columns)
    
    idf["year"] = [dt.year for dt in idf["datetime"]]
    idf["month"] = [dt.month for dt in idf["datetime"]]
    idf["day"] = [dt.day for dt in idf["datetime"]]
    idf["hour"] = [dt.hour for dt in idf["datetime"]]
    idf["minute"] = [dt.minute for dt in idf["datetime"]]
    idf["imputated"] = True
        
    # original dataframe
    #df["imputated"]  = False
    idf = pd.concat([df, idf])
    idf.sort_values("datetime", inplace=True)
    idf.reset_index(inplace=True, drop=True)
    
    idf["datetime-1"] = idf["datetime"].shift(1)
    idf["delta"] = idf["datetime"] - idf["datetime-1"]
    idf["delta"] = idf["delta"].dt.seconds
    #airdata = airdata.assign(delta=lambda x: x["datetime"] - x["datetime-1"])
    #idf["delta"] = [dt.seconds for dt in idf.delta]
    
    #out["imputated"] = True)
    #display(idf)
    
    #display(df_prev.iloc[[0, -2, -1]])
    #display(df.loc[[idx]])
    return  idf

imputation_list = [x for x in reversed(airdata.delta[airdata.delta > 10].index)]
airdata2 = airdata.copy()
for x in imputation_list:
    airdata2 = interpolate_missing(airdata2, x)
    
display(Markdown("Table with missing data:"))
display(airdata2[airdata2.delta > 3])
display(Markdown("It is noteworthy that these values are very small (lesser than 10 seconds) that will be mitigated \
                 with the resampling of the data we will see in next sections."))

/home/jaa6766/.conda/envs/cuda/lib/python3.7/site-packages/ipykernel_launcher.py:29: RuntimeWarning: divide by zero encountered in double_scalars
/home/jaa6766/.conda/envs/cuda/lib/python3.7/site-packages/ipykernel_launcher.py:29: RuntimeWarning: invalid value encountered in double_scalars

CPU times: user 1min, sys: 1min 24s, total: 2min 24s
Wall time: 2min 24s


#airdata2[airdata2.imputated]
(
    ggplot(airdata2[(airdata2.datetime >= "2021-02-18 23:10")][((airdata2.datetime <= "2021-02-18 23:35"))]) +
    geom_point(aes(x="datetime", y="temperature", color="imputated")) +
    theme(axis_text_x=element_text(angle=45)) 
)

/home/jaa6766/.conda/envs/cuda/lib/python3.7/site-packages/ipykernel_launcher.py:4: UserWarning: Boolean Series key will be reindexed to match DataFrame index.

<ggplot: (8729118013281)>


#airdata2[airdata2.imputated]
(
    ggplot(airdata2[(airdata2.datetime >= "2021-02-18 23:10")][((airdata2.datetime <= "2021-02-18 23:35"))]) +
    geom_point(aes(x="datetime", y="gasResistance", color="imputated")) +
    theme(axis_text_x=element_text(angle=45)) 
)

/home/jaa6766/.conda/envs/cuda/lib/python3.7/site-packages/ipykernel_launcher.py:4: UserWarning: Boolean Series key will be reindexed to match DataFrame index.

<ggplot: (8729116297793)>


#airdata2[airdata2.imputated]
(
    ggplot(airdata2[(airdata2.datetime >= "2021-02-18 23:10")][((airdata2.datetime <= "2021-02-18 23:35"))]) +
    geom_point(aes(x="datetime", y="IAQ", color="imputated")) +
    theme(axis_text_x=element_text(angle=45)) 
)

/home/jaa6766/.conda/envs/cuda/lib/python3.7/site-packages/ipykernel_launcher.py:4: UserWarning: Boolean Series key will be reindexed to match DataFrame index.

<ggplot: (8729117893681)>


#airdata2[airdata2.imputated]
(
    ggplot(airdata2[(airdata2.datetime >= "2021-02-18 23:10")][((airdata2.datetime <= "2021-02-18 23:35"))]) +
    geom_point(aes(x="datetime", y="humidity", color="imputated")) +
    theme(axis_text_x=element_text(angle=45)) 
)

/home/jaa6766/.conda/envs/cuda/lib/python3.7/site-packages/ipykernel_launcher.py:4: UserWarning: Boolean Series key will be reindexed to match DataFrame index.

<ggplot: (8729116288241)>


# guardamos el dataframe para utilizarlo
# en las redes neuronales secuenciales
airdata2.to_pickle("data/airdata/air-imputated.pickle.gz")


airdata2 = pd.read_pickle("data/airdata/air-imputated.pickle.gz")
def show_heatmap(data):
    plt.matshow(data.corr().abs())
    plt.xticks(range(data.shape[1]), data.columns, fontsize=14, rotation=90)
    plt.gca().xaxis.tick_bottom()
    plt.yticks(range(data.shape[1]), data.columns, fontsize=14)

    cb = plt.colorbar()
    cb.ax.tick_params(labelsize=14)
    plt.title("Feature Correlation Heatmap", fontsize=14)
    plt.show()

airdata3 = airdata2[["temperature", "pressure", "humidity",
                     "gasResistance", "IAQ", "day", "hour", "minute"]]
show_heatmap(airdata3)


airdata3.corr().abs().round(2)

	temperature	pressure	humidity	gasResistance	IAQ	iaqAccuracy	datetime	year	month	day	hour	minute
0	21.54	777.41	43.93	151328	37.5	1	2021-02-12 06:04:09.089621067	2021	2	12	6	4
1	21.56	777.41	43.89	152702	35.6	1	2021-02-12 06:04:12.087778807	2021	2	12	6	4
2	21.53	777.41	43.97	151328	37.5	1	2021-02-12 06:04:15.072475433	2021	2	12	6	4
3	21.51	777.41	44.03	151464	38.5	1	2021-02-12 06:04:18.070170164	2021	2	12	6	4
4	21.51	777.41	44.05	152425	36.9	1	2021-02-12 06:04:21.061994791	2021	2	12	6	4
...	...	...	...	...	...	...	...	...	...	...	...	...
6285098	25.84	782.96	56.64	928867	130.8	1	2021-09-18 01:20:38.889113188	2021	9	18	1	20
6285099	25.83	782.94	56.66	923130	131.5	1	2021-09-18 01:20:41.882042885	2021	9	18	1	20
6285100	25.83	782.94	56.63	925034	131.3	1	2021-09-18 01:20:44.877856970	2021	9	18	1	20
6285101	25.83	782.94	56.62	923130	131.9	1	2021-09-18 01:20:47.872255564	2021	9	18	1	20
6285102	25.83	782.96	56.63	925034	131.6	1	2021-09-18 01:20:50.866486311	2021	9	18	1	20

	temperature	pressure	humidity	gasResistance	IAQ	iaqAccuracy	datetime	year	month	day	hour	minute	datetime-1	delta	imputated
0	21.51	777.41	44.04	152149	34.7	1	2021-02-12 06:05:35.846304417	2021	2	12	6	5	2021-02-12 06:05:29.856916904	5.0	False
1	21.51	777.41	43.98	152841	33.6	1	2021-02-12 06:05:38.837326527	2021	2	12	6	5	2021-02-12 06:05:35.846304417	2.0	False
2	21.54	777.41	43.73	153259	31.5	1	2021-02-12 06:05:47.812360048	2021	2	12	6	5	2021-02-12 06:05:38.837326527	8.0	False
3	21.53	777.41	43.70	152841	31.5	1	2021-02-12 06:05:50.803695202	2021	2	12	6	5	2021-02-12 06:05:47.812360048	2.0	False
4	21.52	777.41	43.70	153399	30.2	1	2021-02-12 06:05:53.795462847	2021	2	12	6	5	2021-02-12 06:05:50.803695202	2.0	False
5	21.54	777.41	43.77	152702	30.9	1	2021-02-12 06:05:56.786891460	2021	2	12	6	5	2021-02-12 06:05:53.795462847	2.0	False
6	21.55	777.40	43.76	152980	30.7	1	2021-02-12 06:05:59.778601646	2021	2	12	6	5	2021-02-12 06:05:56.786891460	2.0	False
7	21.59	777.40	43.61	152841	30.8	1	2021-02-12 06:06:02.770255804	2021	2	12	6	6	2021-02-12 06:05:59.778601646	2.0	False
8	21.59	777.41	43.56	152980	30.6	1	2021-02-12 06:06:05.761730671	2021	2	12	6	6	2021-02-12 06:06:02.770255804	2.0	False
9	21.63	777.43	43.45	153679	28.8	1	2021-02-12 06:06:08.753019810	2021	2	12	6	6	2021-02-12 06:06:05.761730671	2.0	False

	temperature	pressure	humidity	gasResistance	IAQ	iaqAccuracy	datetime	year	month	day	hour	minute	datetime-1	delta	imputated
0	21.51	777.41	44.04	152149	34.7	1	2021-02-12 06:05:35.846304417	2021	2	12	6	5	2021-02-12 06:05:29.856916904	5.0	False
2	21.54	777.41	43.73	153259	31.5	1	2021-02-12 06:05:47.812360048	2021	2	12	6	5	2021-02-12 06:05:38.837326527	8.0	False
11471	19.95	778.34	43.60	124814	236.8	1	2021-02-12 15:38:09.454870701	2021	2	12	15	38	2021-02-12 15:37:33.558219671	35.0	False
11495	20.41	778.38	42.94	122095	243.2	1	2021-02-12 15:39:24.238069534	2021	2	12	15	39	2021-02-12 15:39:18.254687548	5.0	False
194038	25.24	777.47	25.27	188409	28.7	1	2021-02-18 23:30:02.871531487	2021	2	18	23	30	2021-02-18 23:23:30.376312494	392.0	False
711281	24.44	782.68	29.77	361856	57.8	3	2021-03-08 21:39:54.148881435	2021	3	8	21	39	2021-03-08 21:39:39.176842928	14.0	False
711381	24.80	782.67	30.52	501364	25.0	0	2021-03-08 21:45:12.908920765	2021	3	8	21	45	2021-03-08 21:44:50.610525370	22.0	False
711383	24.67	782.67	30.52	461738	25.0	0	2021-03-08 21:45:35.030911922	2021	3	8	21	45	2021-03-08 21:45:15.906122684	19.0	False
1225137	25.72	781.48	25.65	499500	25.0	0	2021-03-26 17:03:22.019438267	2021	3	26	17	3	2021-03-26 17:02:30.955729723	51.0	False
1816217	27.68	781.48	36.36	1022814	25.0	0	2021-05-21 04:52:55.083148003	2021	5	21	4	52	2021-04-16 04:40:10.764022350	764.0	False
2613223	22.28	779.66	52.13	1187114	25.0	0	2021-06-17 19:17:15.529208899	2021	6	17	19	17	2021-06-17 19:36:00.756372929	85274.0	False
2613257	23.33	779.64	47.13	946517	25.0	0	2021-06-17 19:39:28.145390511	2021	6	17	19	39	2021-06-17 19:18:54.300177336	1233.0	False
2911711	28.23	780.46	32.44	850727	25.0	0	2021-04-16 04:41:09.900250912	2021	4	16	4	41	2021-06-28 03:46:40.487612009	3269.0	False
3921890	21.54	781.15	58.53	1136940	25.0	0	2021-06-28 03:47:29.354257584	2021	6	28	3	47	2021-05-21 04:52:00.783312559	82528.0	False
3921891	21.50	781.15	58.34	1114333	25.0	0	2021-06-28 03:47:50.266227722	2021	6	28	3	47	2021-06-28 03:47:29.354257584	20.0	False
4655880	18.85	787.50	63.51	1167535	25.0	0	2021-07-23 13:44:50.798935652	2021	7	23	13	44	2021-07-23 14:04:10.164503574	85240.0	False
4655911	19.76	787.58	59.56	978894	25.0	0	2021-07-23 14:07:44.666577578	2021	7	23	14	7	2021-07-23 13:46:20.478425026	1284.0	False
5247822	25.03	784.42	55.43	1023592	25.0	0	2021-08-13 02:19:01.038081169	2021	8	13	2	19	2021-08-13 02:18:20.963308096	40.0	False
5247823	25.00	784.44	55.23	1015094	25.0	0	2021-08-13 02:19:27.867798567	2021	8	13	2	19	2021-08-13 02:19:01.038081169	26.0	False
5534130	22.10	779.38	64.59	1161485	25.0	0	2021-08-23 00:17:24.607170820	2021	8	23	0	17	2021-08-23 00:24:10.624371529	85993.0	False
5534160	23.73	779.30	57.95	993353	25.0	0	2021-08-23 00:29:57.714995146	2021	8	23	0	29	2021-08-23 00:18:51.275225639	666.0	False
5671288	20.19	780.64	61.61	1236213	25.0	0	2021-08-27 18:17:15.750379562	2021	8	27	18	17	2021-08-27 18:31:00.879312038	85574.0	False
5671324	21.23	780.62	56.29	970418	25.0	0	2021-08-27 18:34:21.413355350	2021	8	27	18	34	2021-08-27 18:19:00.481747389	920.0	False
5700342	20.09	781.58	63.31	943860	25.0	0	2021-08-28 18:28:06.032265186	2021	8	28	18	28	2021-08-28 18:41:50.590122700	85575.0	False
5700359	21.09	781.60	59.98	926947	25.0	0	2021-08-28 19:08:07.682873249	2021	8	28	19	8	2021-08-28 18:28:53.921931505	2353.0	False
5755798	23.11	780.95	57.93	1069157	25.0	0	2021-08-30 17:15:34.766223192	2021	8	30	17	15	2021-08-30 17:14:20.699152231	74.0	False

	temperature	pressure	humidity	gasResistance	IAQ	iaqAccuracy	year	month	day	hour	minute	delta
count	6285097.00	6285097.00	6285097.00	6285097.00	6285097.00	6285097.00	6285097.0	6285097.00	6285097.00	6285097.00	6285097.00	6285097.00
mean	24.35	781.62	43.44	695073.49	157.43	2.53	2021.0	5.51	15.63	11.51	29.50	2.08
std	2.50	2.19	12.57	314473.23	71.43	0.82	0.0	2.09	8.66	6.92	17.32	83.11
min	16.67	773.78	7.63	76404.00	0.00	0.00	2021.0	2.00	1.00	0.00	0.00	2.00
25%	22.52	780.21	32.68	503242.00	99.00	2.00	2021.0	4.00	8.00	6.00	14.00	2.00
50%	24.21	781.74	43.99	689762.00	172.90	3.00	2021.0	6.00	16.00	12.00	29.00	2.00
75%	26.12	783.16	54.35	863841.00	220.50	3.00	2021.0	7.00	23.00	18.00	44.00	2.00
max	31.29	788.19	71.37	2920738.00	500.00	3.00	2021.0	9.00	31.00	23.00	59.00	85993.00

	temperature	pressure	humidity	gasResistance	IAQ	iaqAccuracy	datetime	year	month	day	hour	minute	datetime-1	delta	imputated
194007	25.67	777.40	24.82	189056	35.0	3	2021-02-18 23:22:00.555237055	2021	2	18	23	22	2021-02-18 23:21:57.560976982	2.0	False
194008	25.66	777.43	24.83	187661	35.9	3	2021-02-18 23:22:03.552131891	2021	2	18	23	22	2021-02-18 23:22:00.555237055	2.0	False
194009	25.66	777.41	24.83	187661	36.4	3	2021-02-18 23:22:06.543661118	2021	2	18	23	22	2021-02-18 23:22:03.552131891	2.0	False
194010	25.64	777.41	24.82	188088	36.5	3	2021-02-18 23:22:09.544224024	2021	2	18	23	22	2021-02-18 23:22:06.543661118	3.0	False
194011	25.63	777.40	24.82	189925	34.8	3	2021-02-18 23:22:12.538087845	2021	2	18	23	22	2021-02-18 23:22:09.544224024	2.0	False
194012	25.64	777.41	24.81	189272	34.3	3	2021-02-18 23:22:15.531872988	2021	2	18	23	22	2021-02-18 23:22:12.538087845	2.0	False
194013	25.62	777.43	24.83	188409	34.8	3	2021-02-18 23:22:18.525767088	2021	2	18	23	22	2021-02-18 23:22:15.531872988	2.0	False
194014	25.61	777.38	24.90	187874	35.6	3	2021-02-18 23:22:21.519450902	2021	2	18	23	22	2021-02-18 23:22:18.525767088	2.0	False
194015	25.63	777.41	24.95	187342	36.6	3	2021-02-18 23:22:24.513052940	2021	2	18	23	22	2021-02-18 23:22:21.519450902	2.0	False
194016	25.64	777.41	24.97	187129	37.5	3	2021-02-18 23:22:27.506954193	2021	2	18	23	22	2021-02-18 23:22:24.513052940	2.0	False
194017	25.64	777.40	24.94	187448	37.8	3	2021-02-18 23:22:30.500771999	2021	2	18	23	22	2021-02-18 23:22:27.506954193	2.0	False
194018	25.64	777.43	24.90	189707	35.9	3	2021-02-18 23:22:33.488167286	2021	2	18	23	22	2021-02-18 23:22:30.500771999	2.0	False
194019	25.63	777.41	24.91	189489	34.7	3	2021-02-18 23:22:36.488412857	2021	2	18	23	22	2021-02-18 23:22:33.488167286	3.0	False
194020	25.63	777.41	24.90	187874	35.5	3	2021-02-18 23:22:39.481906414	2021	2	18	23	22	2021-02-18 23:22:36.488412857	2.0	False
194021	25.63	777.41	24.89	188302	35.7	3	2021-02-18 23:22:42.475703716	2021	2	18	23	22	2021-02-18 23:22:39.481906414	2.0	False
194022	25.61	777.40	24.91	188409	35.7	3	2021-02-18 23:22:45.463096142	2021	2	18	23	22	2021-02-18 23:22:42.475703716	2.0	False
194023	25.59	777.40	24.94	189381	34.8	3	2021-02-18 23:22:48.457271814	2021	2	18	23	22	2021-02-18 23:22:45.463096142	2.0	False
194024	25.60	777.41	25.01	188732	34.7	3	2021-02-18 23:22:51.451401234	2021	2	18	23	22	2021-02-18 23:22:48.457271814	2.0	False
194025	25.61	777.40	25.05	186496	36.8	3	2021-02-18 23:22:54.445565462	2021	2	18	23	22	2021-02-18 23:22:51.451401234	2.0	False
194026	25.64	777.43	25.01	186918	37.7	3	2021-02-18 23:22:57.442430258	2021	2	18	23	22	2021-02-18 23:22:54.445565462	2.0	False
194027	25.67	777.40	25.01	187024	38.2	3	2021-02-18 23:23:00.439792395	2021	2	18	23	23	2021-02-18 23:22:57.442430258	2.0	False
194028	25.68	777.41	24.95	187661	37.9	3	2021-02-18 23:23:03.433859587	2021	2	18	23	23	2021-02-18 23:23:00.439792395	2.0	False
194029	25.68	777.41	24.93	188409	36.9	3	2021-02-18 23:23:06.427474499	2021	2	18	23	23	2021-02-18 23:23:03.433859587	2.0	False
194030	25.69	777.40	24.90	187024	37.6	3	2021-02-18 23:23:09.421072960	2021	2	18	23	23	2021-02-18 23:23:06.427474499	2.0	False
194031	25.70	777.43	24.89	186812	38.3	3	2021-02-18 23:23:12.414887190	2021	2	18	23	23	2021-02-18 23:23:09.421072960	2.0	False
194032	25.70	777.43	24.91	188088	37.5	3	2021-02-18 23:23:15.408778191	2021	2	18	23	23	2021-02-18 23:23:12.414887190	2.0	False
194033	25.71	777.43	24.92	187767	37.2	3	2021-02-18 23:23:18.402513504	2021	2	18	23	23	2021-02-18 23:23:15.408778191	2.0	False
194034	25.69	777.43	24.95	186181	38.5	3	2021-02-18 23:23:21.390101194	2021	2	18	23	23	2021-02-18 23:23:18.402513504	2.0	False
194035	25.69	777.43	24.89	187235	38.4	3	2021-02-18 23:23:24.390413523	2021	2	18	23	23	2021-02-18 23:23:21.390101194	3.0	False
194036	25.69	777.43	24.85	189272	36.4	3	2021-02-18 23:23:27.384147644	2021	2	18	23	23	2021-02-18 23:23:24.390413523	2.0	False
194037	25.71	777.41	24.77	187661	36.7	3	2021-02-18 23:23:30.376312494	2021	2	18	23	23	2021-02-18 23:23:27.384147644	2.0	False
194038	25.24	777.47	25.27	188409	28.7	1	2021-02-18 23:30:02.871531487	2021	2	18	23	30	2021-02-18 23:23:30.376312494	392.0	False

Imputations for Missing Data on Our Sensor.¶

Missing Data¶

Missing Data Visualitzations¶

Imputations¶

Imputated Values Visualizations¶

Relationships Between Variables¶

References¶

	temperature	pressure	humidity	gasResistance	IAQ	day	hour	minute
temperature	1.00	0.35	0.58	0.09	0.01	0.14	0.04	0.0
pressure	0.35	1.00	0.45	0.04	0.24	0.08	0.20	0.0
humidity	0.58	0.45	1.00	0.32	0.05	0.15	0.07	0.0
gasResistance	0.09	0.04	0.32	1.00	0.38	0.05	0.21	0.0
IAQ	0.01	0.24	0.05	0.38	1.00	0.02	0.28	0.0
day	0.14	0.08	0.15	0.05	0.02	1.00	0.00	0.0
hour	0.04	0.20	0.07	0.21	0.28	0.00	1.00	0.0
minute	0.00	0.00	0.00	0.00	0.00	0.00	0.00	1.0