Some tips on splicing and dicing Pandas DataFrames

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import datetime
print('Last updated: {}'.format(datetime.date.today().strftime('%d %B, %Y')))

Last updated: 05 July, 2019

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Outline:

Pandas DataFrames (two-dimensional) and Series (one-dimensional) data structures are extensively used in the brightwind library for the storage, transfer and display of data. A simple explaination is a Series is simply a single column of data which can have a index and a DataFrame is multiple columns of data with an index. Both are explained in more detail here:

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html

For an easier intro to them you could check out the below link. Needless to say, Pandas makes Python a powerful data analysis tool.

https://www.geeksforgeeks.org/python-pandas-dataframe/

This tutorial will give a basic introduction to using these data structures and outline how to:

1. Seperate specific columns from DataFrames into new DataFrames and Series

2. Select specific ranges from DataFrames and Series using the index

3. Search for a specific entry in a DataFrame or Series

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1: Selecting Columns

Data can be read into DataFrames easily from excel and csv files easily using the brightwind functions load_csv() and load_excel(). In the example below, data is read from the csv file demo_data into the DataFrame data.

import brightwind as bw
data = bw.load_csv(r'C:\Users\Stephen\Documents\Analysis\demo_data.csv')
data.head(5)

	Spd80mN	Spd80mS	Spd60mN	Spd60mS	Spd40mN	Spd40mS	Spd80mNStd	Spd80mSStd	Spd60mNStd	Spd60mSStd	...	Dir78mSStd	Dir58mS	Dir58mSStd	Dir38mS	Dir38mSStd	T2m	RH2m	P2m	PrcpTot	BattMin
Timestamp
2016-01-09 15:30:00	8.370	7.911	8.160	7.849	7.857	7.626	1.240	1.075	1.060	0.947	...	6.100	110.1	6.009	112.2	5.724	0.711	100.0	935.0	0.0	12.94
2016-01-09 15:40:00	8.250	7.961	8.100	7.884	7.952	7.840	0.897	0.875	0.900	0.855	...	5.114	110.9	4.702	109.8	5.628	0.630	100.0	935.0	0.0	12.95
2016-01-09 17:00:00	7.652	7.545	7.671	7.551	7.531	7.457	0.756	0.703	0.797	0.749	...	4.172	113.1	3.447	111.8	4.016	1.126	100.0	934.0	0.0	12.75
2016-01-09 17:10:00	7.382	7.325	6.818	6.689	6.252	6.174	0.844	0.810	0.897	0.875	...	4.680	118.8	5.107	115.6	5.189	0.954	100.0	934.0	0.0	12.71
2016-01-09 17:20:00	7.977	7.791	8.110	7.915	8.140	7.974	0.556	0.528	0.562	0.524	...	3.123	115.9	2.960	113.6	3.540	0.863	100.0	934.0	0.0	12.69

5 rows × 29 columns

Once this data is loaded, the different columns and rows in the dataframe can be isolated for use in other calculations. To isolate the first column, Spd80mN, from the DataFrame data into the Series Wspd80mN the command is:

Wspd80mN = data['Spd80mN']

The series Wspd80mN can then be easily passed into a function such as monthly_means():

bw.monthly_means(Wspd80mN)

Similarly, you can select a few columns at the same time.

bw.monthly_means(data[['T2m', 'BattMin']])

Another way to select a single column is to use the data.Spd80mN. This only works if you column names have no spaces in them.

bw.monthly_means(data.Spd80mN)

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Step 2: Selecting Ranges

Ranges, or rows, from within a Series or DataFrame can be seleced. The brightwind library always asigns the index of the DataFrame to be a datetime data type making it much easier to work with the DataFrame for wind analysis purposes. For example, to select data points between two dates for the data DataFrame we loaded in:

data['2017-01-01':'2017-02-01']

	Spd80mN	Spd80mS	Spd60mN	Spd60mS	Spd40mN	Spd40mS	Spd80mNStd	Spd80mSStd	Spd60mNStd	Spd60mSStd	...	Dir78mSStd	Dir58mS	Dir58mSStd	Dir38mS	Dir38mSStd	T2m	RH2m	P2m	PrcpTot	BattMin
Timestamp
2017-01-01 00:00:00	5.876	5.858	5.747	5.744	5.605	5.558	1.160	1.109	1.186	1.158	...	7.025	275.2	0.0	324.900	6.451	3.077	99.7	966.0	0.0	12.71
2017-01-01 00:10:00	5.911	5.898	5.539	5.621	5.324	5.345	1.114	1.079	1.005	0.977	...	7.498	275.2	0.0	319.600	9.160	2.915	99.8	966.0	0.0	12.71
2017-01-01 00:20:00	7.004	6.967	6.741	6.797	6.645	6.634	1.038	0.998	1.042	1.033	...	6.283	275.2	0.0	316.500	7.228	2.865	99.8	966.0	0.0	12.71
2017-01-01 00:30:00	7.079	7.067	6.832	6.881	6.792	6.801	0.705	0.667	0.707	0.665	...	4.661	275.2	0.0	319.100	5.279	2.693	97.8	966.0	0.0	12.71
2017-01-01 00:40:00	6.796	6.765	6.684	6.742	6.658	6.685	0.794	0.785	0.812	0.792	...	5.036	275.2	0.0	317.300	6.070	2.471	98.4	967.0	0.0	12.71
2017-01-01 00:50:00	8.380	8.350	8.070	8.170	7.861	7.915	0.999	0.949	0.920	0.871	...	5.734	275.2	0.0	314.400	5.881	2.329	99.1	966.0	0.0	12.70
2017-01-01 01:00:00	8.910	8.870	8.310	8.400	7.992	7.991	0.751	0.717	0.809	0.775	...	3.719	275.2	0.0	315.900	4.887	2.137	98.5	966.0	0.0	12.70
2017-01-01 01:10:00	8.490	8.460	8.010	8.050	7.507	7.526	0.720	0.665	0.779	0.756	...	4.544	275.2	0.0	323.500	5.816	2.006	99.0	966.0	0.0	12.70
2017-01-01 01:20:00	7.116	7.096	6.755	6.739	6.410	6.354	0.719	0.678	0.741	0.700	...	5.676	275.2	0.0	326.100	5.782	1.783	98.9	966.0	0.0	12.70
2017-01-01 01:30:00	8.060	8.070	7.415	7.446	6.812	6.814	0.867	0.835	0.912	0.827	...	5.352	275.2	0.0	323.600	6.129	1.742	99.6	966.0	0.0	12.70
2017-01-01 01:40:00	9.150	9.170	8.480	8.300	7.883	7.721	0.738	0.685	0.789	0.750	...	5.377	275.2	0.0	330.600	6.721	1.763	99.8	966.0	0.1	12.70
2017-01-01 01:50:00	7.837	7.820	7.132	6.473	6.608	6.100	0.837	0.834	1.013	1.140	...	14.100	275.2	0.0	339.700	6.033	1.783	99.3	966.0	0.0	12.69
2017-01-01 02:00:00	6.234	6.149	5.446	4.847	4.863	4.624	1.133	1.067	0.962	0.795	...	20.510	275.2	0.0	2.726	17.940	1.773	99.3	966.0	0.0	12.69
2017-01-01 02:10:00	5.896	5.872	5.318	4.967	4.892	4.778	0.573	0.528	0.640	0.635	...	14.600	275.2	0.0	3.750	15.530	1.641	100.0	966.0	0.0	12.69
2017-01-01 02:20:00	4.897	4.877	4.583	4.469	4.272	4.250	0.759	0.696	0.731	0.676	...	14.260	275.2	0.0	15.830	17.620	1.510	100.0	966.0	0.0	12.69
2017-01-01 02:30:00	4.830	4.859	4.135	4.106	3.775	3.751	0.797	0.739	0.871	0.831	...	6.807	275.2	0.0	45.140	8.820	1.541	100.0	967.0	0.0	12.69
2017-01-01 02:40:00	4.728	4.716	4.353	4.322	4.006	4.001	0.510	0.433	0.470	0.396	...	7.454	275.2	0.0	36.230	7.463	1.551	100.0	967.0	0.0	12.69
2017-01-01 02:50:00	4.207	4.221	3.646	3.563	3.105	3.008	1.211	1.228	1.162	1.235	...	12.380	275.2	0.0	9.620	20.540	1.398	100.0	967.0	0.0	12.69
2017-01-01 03:00:00	4.315	4.267	3.923	3.754	3.590	3.515	0.569	0.511	0.529	0.516	...	11.910	275.2	0.0	5.029	10.810	1.470	100.0	967.0	0.0	12.69
2017-01-01 03:10:00	5.976	5.952	5.513	5.359	4.838	4.746	0.717	0.667	0.839	0.935	...	9.510	275.2	0.0	6.449	14.240	1.530	100.0	967.0	0.0	12.68
2017-01-01 03:20:00	6.539	6.550	5.985	5.969	5.471	5.467	0.803	0.761	0.745	0.741	...	4.772	275.2	0.0	10.840	11.510	1.439	100.0	967.0	0.0	12.68
2017-01-01 03:30:00	7.523	7.566	7.091	7.106	6.509	6.521	0.549	0.484	0.576	0.516	...	2.797	275.2	0.0	12.380	4.930	1.500	100.0	967.0	0.0	12.68
2017-01-01 03:40:00	7.904	7.953	7.640	7.637	7.220	7.215	0.497	0.428	0.656	0.616	...	2.781	275.2	0.0	18.510	5.389	1.570	100.0	967.0	0.0	12.67
2017-01-01 03:50:00	7.621	7.626	7.261	7.189	6.824	6.801	0.683	0.637	0.750	0.725	...	9.960	275.2	0.0	14.650	8.850	1.520	99.9	967.0	0.0	12.67
2017-01-01 04:00:00	7.791	7.850	7.277	7.276	6.593	6.618	1.116	1.056	1.153	1.108	...	6.700	275.2	0.0	11.620	11.890	1.379	100.0	967.0	0.0	12.67
2017-01-01 04:10:00	8.190	8.230	7.611	7.636	7.090	7.111	1.079	1.003	1.251	1.249	...	8.580	275.2	0.0	13.020	11.540	1.288	100.0	967.0	0.0	12.67
2017-01-01 04:20:00	8.200	8.240	7.179	7.210	6.591	6.635	1.491	1.428	1.501	1.466	...	8.820	275.2	0.0	16.440	11.740	1.136	98.5	967.0	0.0	12.67
2017-01-01 04:30:00	8.290	8.370	7.685	7.707	6.911	6.920	0.996	0.948	1.137	1.117	...	9.960	275.2	0.0	15.710	14.230	1.045	97.5	968.0	0.0	12.67
2017-01-01 04:40:00	8.230	8.290	7.376	7.299	6.835	6.849	1.071	1.033	1.231	1.126	...	12.660	275.2	0.0	10.550	13.360	0.792	96.0	968.0	0.0	12.67
2017-01-01 04:50:00	9.600	9.660	8.890	8.850	8.170	8.190	1.075	1.040	1.033	1.094	...	6.267	275.2	0.0	8.210	15.310	0.468	95.4	968.0	0.0	12.67
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2017-02-01 19:00:00	9.980	9.970	7.821	8.970	7.269	8.350	1.556	1.471	1.402	1.335	...	5.712	275.2	0.0	170.500	6.764	4.370	98.8	947.0	0.0	12.98
2017-02-01 19:10:00	7.264	7.333	5.912	6.739	5.534	6.465	1.459	1.326	1.319	1.074	...	8.560	275.2	0.0	170.900	7.869	4.512	97.3	946.0	0.0	12.98
2017-02-01 19:20:00	8.210	8.270	6.920	7.266	6.077	6.393	1.875	1.738	1.835	1.572	...	10.950	275.2	0.0	160.700	11.660	4.814	96.8	947.0	0.0	12.97
2017-02-01 19:30:00	11.040	10.980	10.010	9.960	9.180	9.180	1.520	1.435	1.551	1.421	...	5.745	275.2	0.0	156.000	6.775	5.138	95.4	946.0	0.0	12.96
2017-02-01 19:40:00	12.240	12.100	11.570	11.390	11.040	10.870	1.440	1.362	1.444	1.332	...	5.474	275.2	0.0	153.500	6.161	5.562	95.5	946.0	0.0	12.96
2017-02-01 19:50:00	13.340	13.250	12.650	12.490	11.990	11.830	1.632	1.571	1.846	1.659	...	5.866	275.2	0.0	152.800	6.037	5.583	94.3	946.0	0.0	12.95
2017-02-01 20:00:00	14.150	14.070	13.570	13.280	12.890	12.670	1.196	1.103	1.161	1.076	...	5.031	275.2	0.0	150.800	5.244	5.623	94.8	945.0	0.0	12.94
2017-02-01 20:10:00	15.540	15.460	14.930	14.700	14.450	14.280	1.321	1.245	1.497	1.374	...	4.250	275.2	0.0	148.200	5.148	5.461	95.1	945.0	0.0	12.93
2017-02-01 20:20:00	14.870	14.860	14.280	14.060	13.850	13.650	1.292	1.204	1.556	1.457	...	3.911	275.2	0.0	146.800	4.782	5.421	97.1	945.0	0.0	12.93
2017-02-01 20:30:00	15.220	15.220	14.920	14.750	14.340	14.190	1.244	1.174	1.198	1.143	...	4.193	275.2	0.0	146.100	4.854	5.502	97.8	945.0	0.0	12.93
2017-02-01 20:40:00	14.510	14.500	14.230	14.030	13.760	13.600	1.362	1.273	1.459	1.373	...	4.882	275.2	0.0	148.500	4.689	5.542	98.3	945.0	0.0	12.92
2017-02-01 20:50:00	13.570	13.510	13.110	12.910	12.690	12.550	1.465	1.380	1.592	1.458	...	5.153	275.2	0.0	152.800	5.701	5.461	99.1	945.0	0.1	12.92
2017-02-01 21:00:00	13.200	13.120	12.100	12.080	11.540	11.540	1.683	1.569	1.678	1.478	...	6.637	275.2	0.0	155.700	6.759	5.592	100.0	945.0	0.1	12.91
2017-02-01 21:10:00	12.780	12.840	11.880	11.990	11.040	11.270	2.181	2.064	2.074	1.846	...	6.751	275.2	0.0	156.200	8.080	5.572	100.0	945.0	0.1	12.91
2017-02-01 21:20:00	13.230	13.190	13.010	12.900	12.680	12.670	1.995	1.907	1.828	1.734	...	7.287	275.2	0.0	153.200	6.439	5.602	100.0	945.0	0.1	12.91
2017-02-01 21:30:00	12.160	12.210	11.190	11.450	10.670	10.940	2.316	2.182	2.726	2.271	...	8.080	275.2	0.0	158.100	8.290	5.592	100.0	945.0	0.2	12.91
2017-02-01 21:40:00	11.400	11.690	9.840	10.690	9.060	9.860	1.785	1.759	1.655	1.527	...	8.090	275.2	0.0	163.600	9.410	5.592	100.0	944.0	0.2	12.90
2017-02-01 21:50:00	11.530	11.770	10.270	10.910	9.930	10.460	2.281	2.186	2.116	2.102	...	9.460	275.2	0.0	159.600	11.100	5.673	100.0	945.0	0.2	12.90
2017-02-01 22:00:00	11.940	12.190	10.120	11.330	9.520	11.000	2.337	2.232	2.613	2.416	...	8.220	275.2	0.0	168.200	7.792	5.531	100.0	944.0	0.2	12.89
2017-02-01 22:10:00	11.850	12.060	9.970	11.290	9.230	10.570	1.937	1.833	2.190	1.828	...	8.860	275.2	0.0	170.700	8.850	5.421	100.0	944.0	0.1	12.89
2017-02-01 22:20:00	10.240	10.390	8.220	9.700	7.887	9.290	1.740	1.657	1.474	1.462	...	8.550	275.2	0.0	176.200	8.810	5.279	100.0	944.0	0.0	12.89
2017-02-01 22:30:00	11.000	11.050	8.270	10.050	8.110	9.410	1.421	1.318	1.443	1.579	...	6.332	275.2	0.0	181.800	7.599	5.309	100.0	944.0	0.0	12.89
2017-02-01 22:40:00	11.900	12.000	9.100	11.060	8.720	10.270	1.561	1.454	1.584	1.438	...	6.540	275.2	0.0	175.800	8.180	5.411	100.0	944.0	0.0	12.89
2017-02-01 22:50:00	11.160	11.250	8.280	10.310	7.959	9.430	2.197	2.084	1.699	1.934	...	6.301	275.2	0.0	176.700	8.300	5.481	100.0	944.0	0.0	12.88
2017-02-01 23:00:00	10.580	10.760	8.310	9.800	8.080	9.270	1.974	1.819	1.733	1.689	...	8.480	275.2	0.0	172.100	8.700	5.552	100.0	944.0	0.1	12.88
2017-02-01 23:10:00	11.190	11.410	8.710	10.480	8.240	9.790	1.821	1.691	1.608	1.472	...	7.089	275.2	0.0	172.200	6.555	5.491	100.0	944.0	0.0	12.88
2017-02-01 23:20:00	11.580	11.750	9.150	10.820	8.840	10.120	2.490	2.315	2.191	2.361	...	7.986	275.2	0.0	180.000	8.520	5.542	100.0	944.0	0.1	12.88
2017-02-01 23:30:00	14.320	14.490	10.770	13.280	10.430	12.200	2.221	1.981	1.867	1.856	...	6.347	275.2	0.0	177.700	7.901	5.602	100.0	944.0	0.0	12.88
2017-02-01 23:40:00	12.870	12.860	10.650	12.010	10.250	11.320	2.060	1.905	1.994	1.764	...	7.929	275.2	0.0	185.400	9.620	5.643	100.0	944.0	0.0	12.88
2017-02-01 23:50:00	13.330	13.440	10.920	12.630	10.640	11.770	2.425	2.258	2.454	2.119	...	7.456	275.2	0.0	183.100	8.470	5.643	100.0	944.0	0.2	12.88

4608 rows × 29 columns

To select all the data points from a specific date, i.e. 2017-01-01, to the end of the series you can leave the area after the ‘:’ empty.

data['2017-01-01':]

	Spd80mN	Spd80mS	Spd60mN	Spd60mS	Spd40mN	Spd40mS	Spd80mNStd	Spd80mSStd	Spd60mNStd	Spd60mSStd	...	Dir78mSStd	Dir58mS	Dir58mSStd	Dir38mS	Dir38mSStd	T2m	RH2m	P2m	PrcpTot	BattMin
Timestamp
2017-01-01 00:00:00	5.876	5.858	5.747	5.744	5.605	5.558	1.160	1.109	1.186	1.158	...	7.025	275.2	0.0	324.900	6.451	3.077	99.7	966.0	0.0	12.71
2017-01-01 00:10:00	5.911	5.898	5.539	5.621	5.324	5.345	1.114	1.079	1.005	0.977	...	7.498	275.2	0.0	319.600	9.160	2.915	99.8	966.0	0.0	12.71
2017-01-01 00:20:00	7.004	6.967	6.741	6.797	6.645	6.634	1.038	0.998	1.042	1.033	...	6.283	275.2	0.0	316.500	7.228	2.865	99.8	966.0	0.0	12.71
2017-01-01 00:30:00	7.079	7.067	6.832	6.881	6.792	6.801	0.705	0.667	0.707	0.665	...	4.661	275.2	0.0	319.100	5.279	2.693	97.8	966.0	0.0	12.71
2017-01-01 00:40:00	6.796	6.765	6.684	6.742	6.658	6.685	0.794	0.785	0.812	0.792	...	5.036	275.2	0.0	317.300	6.070	2.471	98.4	967.0	0.0	12.71
2017-01-01 00:50:00	8.380	8.350	8.070	8.170	7.861	7.915	0.999	0.949	0.920	0.871	...	5.734	275.2	0.0	314.400	5.881	2.329	99.1	966.0	0.0	12.70
2017-01-01 01:00:00	8.910	8.870	8.310	8.400	7.992	7.991	0.751	0.717	0.809	0.775	...	3.719	275.2	0.0	315.900	4.887	2.137	98.5	966.0	0.0	12.70
2017-01-01 01:10:00	8.490	8.460	8.010	8.050	7.507	7.526	0.720	0.665	0.779	0.756	...	4.544	275.2	0.0	323.500	5.816	2.006	99.0	966.0	0.0	12.70
2017-01-01 01:20:00	7.116	7.096	6.755	6.739	6.410	6.354	0.719	0.678	0.741	0.700	...	5.676	275.2	0.0	326.100	5.782	1.783	98.9	966.0	0.0	12.70
2017-01-01 01:30:00	8.060	8.070	7.415	7.446	6.812	6.814	0.867	0.835	0.912	0.827	...	5.352	275.2	0.0	323.600	6.129	1.742	99.6	966.0	0.0	12.70
2017-01-01 01:40:00	9.150	9.170	8.480	8.300	7.883	7.721	0.738	0.685	0.789	0.750	...	5.377	275.2	0.0	330.600	6.721	1.763	99.8	966.0	0.1	12.70
2017-01-01 01:50:00	7.837	7.820	7.132	6.473	6.608	6.100	0.837	0.834	1.013	1.140	...	14.100	275.2	0.0	339.700	6.033	1.783	99.3	966.0	0.0	12.69
2017-01-01 02:00:00	6.234	6.149	5.446	4.847	4.863	4.624	1.133	1.067	0.962	0.795	...	20.510	275.2	0.0	2.726	17.940	1.773	99.3	966.0	0.0	12.69
2017-01-01 02:10:00	5.896	5.872	5.318	4.967	4.892	4.778	0.573	0.528	0.640	0.635	...	14.600	275.2	0.0	3.750	15.530	1.641	100.0	966.0	0.0	12.69
2017-01-01 02:20:00	4.897	4.877	4.583	4.469	4.272	4.250	0.759	0.696	0.731	0.676	...	14.260	275.2	0.0	15.830	17.620	1.510	100.0	966.0	0.0	12.69
2017-01-01 02:30:00	4.830	4.859	4.135	4.106	3.775	3.751	0.797	0.739	0.871	0.831	...	6.807	275.2	0.0	45.140	8.820	1.541	100.0	967.0	0.0	12.69
2017-01-01 02:40:00	4.728	4.716	4.353	4.322	4.006	4.001	0.510	0.433	0.470	0.396	...	7.454	275.2	0.0	36.230	7.463	1.551	100.0	967.0	0.0	12.69
2017-01-01 02:50:00	4.207	4.221	3.646	3.563	3.105	3.008	1.211	1.228	1.162	1.235	...	12.380	275.2	0.0	9.620	20.540	1.398	100.0	967.0	0.0	12.69
2017-01-01 03:00:00	4.315	4.267	3.923	3.754	3.590	3.515	0.569	0.511	0.529	0.516	...	11.910	275.2	0.0	5.029	10.810	1.470	100.0	967.0	0.0	12.69
2017-01-01 03:10:00	5.976	5.952	5.513	5.359	4.838	4.746	0.717	0.667	0.839	0.935	...	9.510	275.2	0.0	6.449	14.240	1.530	100.0	967.0	0.0	12.68
2017-01-01 03:20:00	6.539	6.550	5.985	5.969	5.471	5.467	0.803	0.761	0.745	0.741	...	4.772	275.2	0.0	10.840	11.510	1.439	100.0	967.0	0.0	12.68
2017-01-01 03:30:00	7.523	7.566	7.091	7.106	6.509	6.521	0.549	0.484	0.576	0.516	...	2.797	275.2	0.0	12.380	4.930	1.500	100.0	967.0	0.0	12.68
2017-01-01 03:40:00	7.904	7.953	7.640	7.637	7.220	7.215	0.497	0.428	0.656	0.616	...	2.781	275.2	0.0	18.510	5.389	1.570	100.0	967.0	0.0	12.67
2017-01-01 03:50:00	7.621	7.626	7.261	7.189	6.824	6.801	0.683	0.637	0.750	0.725	...	9.960	275.2	0.0	14.650	8.850	1.520	99.9	967.0	0.0	12.67
2017-01-01 04:00:00	7.791	7.850	7.277	7.276	6.593	6.618	1.116	1.056	1.153	1.108	...	6.700	275.2	0.0	11.620	11.890	1.379	100.0	967.0	0.0	12.67
2017-01-01 04:10:00	8.190	8.230	7.611	7.636	7.090	7.111	1.079	1.003	1.251	1.249	...	8.580	275.2	0.0	13.020	11.540	1.288	100.0	967.0	0.0	12.67
2017-01-01 04:20:00	8.200	8.240	7.179	7.210	6.591	6.635	1.491	1.428	1.501	1.466	...	8.820	275.2	0.0	16.440	11.740	1.136	98.5	967.0	0.0	12.67
2017-01-01 04:30:00	8.290	8.370	7.685	7.707	6.911	6.920	0.996	0.948	1.137	1.117	...	9.960	275.2	0.0	15.710	14.230	1.045	97.5	968.0	0.0	12.67
2017-01-01 04:40:00	8.230	8.290	7.376	7.299	6.835	6.849	1.071	1.033	1.231	1.126	...	12.660	275.2	0.0	10.550	13.360	0.792	96.0	968.0	0.0	12.67
2017-01-01 04:50:00	9.600	9.660	8.890	8.850	8.170	8.190	1.075	1.040	1.033	1.094	...	6.267	275.2	0.0	8.210	15.310	0.468	95.4	968.0	0.0	12.67
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2017-11-23 06:00:00	10.620	0.000	10.420	10.370	10.110	10.030	0.857	0.000	0.803	0.752	...	0.000	275.2	0.0	257.200	4.303	0.589	100.0	940.0	0.1	12.71
2017-11-23 06:10:00	10.720	0.000	10.670	10.600	10.470	10.380	1.027	0.000	0.938	0.919	...	0.000	275.2	0.0	258.700	4.351	0.701	100.0	940.0	0.0	12.71
2017-11-23 06:20:00	10.940	0.000	10.650	10.600	10.290	10.210	0.708	0.000	0.823	0.778	...	0.000	275.2	0.0	257.700	3.923	0.761	99.5	940.0	0.0	12.71
2017-11-23 06:30:00	10.270	0.000	10.130	9.960	9.910	9.790	0.800	0.000	0.865	0.808	...	0.000	275.2	0.0	251.600	3.722	0.792	98.0	940.0	0.0	12.71
2017-11-23 06:40:00	8.180	0.000	7.985	7.811	7.649	7.519	1.167	0.000	1.239	1.199	...	0.000	275.2	0.0	242.800	4.858	0.518	98.2	941.0	0.0	12.71
2017-11-23 06:50:00	8.870	0.000	8.570	8.330	8.020	7.873	0.676	0.000	0.582	0.534	...	0.000	275.2	0.0	232.900	3.426	0.782	99.9	941.0	0.0	12.71
2017-11-23 07:00:00	9.300	0.000	8.950	8.670	8.590	8.400	0.933	0.000	0.994	0.937	...	0.000	275.2	0.0	229.300	4.575	0.761	99.5	941.0	0.0	12.71
2017-11-23 07:10:00	8.130	0.000	7.863	7.592	7.283	7.094	0.823	0.000	0.837	0.794	...	0.000	275.2	0.0	225.100	4.478	0.721	99.9	941.0	0.0	12.67
2017-11-23 07:20:00	8.910	0.000	8.500	8.250	8.020	7.864	1.063	0.000	0.977	0.929	...	0.000	275.2	0.0	226.700	5.236	0.751	100.0	941.0	0.0	12.66
2017-11-23 07:30:00	9.610	0.000	9.260	8.980	8.630	8.470	0.942	0.000	0.923	0.860	...	0.000	275.2	0.0	228.100	5.127	0.670	100.0	941.0	0.0	12.66
2017-11-23 07:40:00	10.900	0.000	10.310	10.050	9.680	9.490	1.268	0.000	1.170	1.111	...	0.000	275.2	0.0	232.700	5.563	0.570	100.0	941.0	0.0	12.67
2017-11-23 07:50:00	11.560	0.000	11.080	10.790	10.400	10.190	1.022	0.000	1.114	1.067	...	0.000	275.2	0.0	238.600	4.363	0.751	100.0	941.0	0.0	12.70
2017-11-23 08:00:00	11.730	0.000	11.290	11.030	10.690	10.490	0.842	0.000	0.834	0.759	...	0.000	275.2	0.0	237.400	3.395	0.771	100.0	941.0	0.0	12.70
2017-11-23 08:10:00	11.700	0.000	11.420	11.130	11.030	10.830	0.764	0.000	0.727	0.673	...	0.000	275.2	0.0	235.900	3.391	0.863	99.7	941.0	0.0	12.66
2017-11-23 08:20:00	12.270	0.000	12.020	11.760	11.510	11.330	0.774	0.000	0.800	0.755	...	0.000	275.2	0.0	237.000	3.657	0.842	99.5	941.0	0.0	12.66
2017-11-23 08:30:00	11.510	0.000	11.260	10.990	10.740	10.530	0.956	0.000	0.903	0.857	...	0.000	275.2	0.0	234.600	3.988	0.701	99.3	942.0	0.0	12.65
2017-11-23 08:40:00	11.380	0.000	10.850	10.520	10.310	10.090	0.962	0.000	0.867	0.840	...	0.000	275.2	0.0	235.000	4.448	0.751	99.7	942.0	0.0	12.68
2017-11-23 08:50:00	10.420	0.000	10.090	9.810	9.590	9.380	1.034	0.000	1.141	1.111	...	0.000	275.2	0.0	223.700	7.394	0.680	99.7	942.0	0.0	12.69
2017-11-23 09:00:00	9.050	0.000	8.530	8.270	7.696	7.503	0.668	0.000	0.763	0.681	...	0.000	275.2	0.0	211.600	7.172	0.812	100.0	942.0	0.0	12.70
2017-11-23 09:10:00	7.484	0.000	7.231	6.956	6.596	6.404	1.009	0.000	0.868	0.811	...	0.000	275.2	0.0	213.000	7.851	0.771	98.4	942.0	0.0	12.69
2017-11-23 09:20:00	7.228	0.000	6.903	6.691	6.273	6.089	0.756	0.000	0.841	0.753	...	0.000	275.2	0.0	209.100	7.654	0.549	99.0	943.0	0.0	12.70
2017-11-23 09:30:00	7.740	0.000	7.359	7.147	6.889	6.775	0.634	0.000	0.618	0.586	...	0.000	275.2	0.0	221.000	6.078	0.711	99.6	943.0	0.0	12.52
2017-11-23 09:40:00	8.380	0.000	7.900	7.675	7.190	7.068	0.822	0.000	0.881	0.798	...	0.000	275.2	0.0	221.900	4.969	0.651	98.8	943.0	0.0	12.73
2017-11-23 09:50:00	9.870	0.000	9.250	8.970	8.450	8.250	0.954	0.000	0.878	0.805	...	0.000	275.2	0.0	220.200	5.269	0.873	99.8	943.0	0.0	12.89
2017-11-23 10:00:00	9.800	0.000	9.340	9.070	8.630	8.450	1.170	0.000	1.136	1.076	...	0.000	275.2	0.0	221.900	5.125	0.731	98.6	943.0	0.0	12.94
2017-11-23 10:10:00	10.480	0.000	10.190	9.890	9.590	9.420	0.720	0.000	0.733	0.668	...	0.000	275.2	0.0	222.200	4.111	0.943	99.7	943.0	0.0	13.02
2017-11-23 10:20:00	9.390	0.000	9.120	8.850	8.520	8.340	0.659	0.000	0.734	0.651	...	0.000	275.2	0.0	218.400	4.817	0.792	98.6	943.0	0.0	13.69
2017-11-23 10:30:00	9.140	0.000	8.700	8.450	8.030	7.875	0.689	0.000	0.821	0.732	...	0.000	275.2	0.0	216.000	5.784	0.802	100.0	943.0	0.0	13.86
2017-11-23 10:40:00	7.927	0.000	7.383	7.159	6.811	6.668	0.817	0.000	0.769	0.692	...	0.000	275.2	0.0	219.500	5.051	0.883	100.0	943.0	0.0	13.80
2017-11-23 10:50:00	7.120	0.000	6.617	6.404	5.865	5.749	0.537	0.000	0.534	0.450	...	0.000	275.2	0.0	222.400	4.902	0.802	100.0	944.0	0.0	13.71

47010 rows × 29 columns

To select all the data points from the start to a specific date, i.e. 2017-01-01, you can leave the area before the ‘:’ empty.

data[:'2017-01-01']

	Spd80mN	Spd80mS	Spd60mN	Spd60mS	Spd40mN	Spd40mS	Spd80mNStd	Spd80mSStd	Spd60mNStd	Spd60mSStd	...	Dir78mSStd	Dir58mS	Dir58mSStd	Dir38mS	Dir38mSStd	T2m	RH2m	P2m	PrcpTot	BattMin
Timestamp
2016-01-09 15:30:00	8.370	7.911	8.160	7.849	7.857	7.626	1.240	1.075	1.060	0.947	...	6.100	110.10	6.009	112.200	5.724	0.711	100.0	935.0	0.0	12.94
2016-01-09 15:40:00	8.250	7.961	8.100	7.884	7.952	7.840	0.897	0.875	0.900	0.855	...	5.114	110.90	4.702	109.800	5.628	0.630	100.0	935.0	0.0	12.95
2016-01-09 17:00:00	7.652	7.545	7.671	7.551	7.531	7.457	0.756	0.703	0.797	0.749	...	4.172	113.10	3.447	111.800	4.016	1.126	100.0	934.0	0.0	12.75
2016-01-09 17:10:00	7.382	7.325	6.818	6.689	6.252	6.174	0.844	0.810	0.897	0.875	...	4.680	118.80	5.107	115.600	5.189	0.954	100.0	934.0	0.0	12.71
2016-01-09 17:20:00	7.977	7.791	8.110	7.915	8.140	7.974	0.556	0.528	0.562	0.524	...	3.123	115.90	2.960	113.600	3.540	0.863	100.0	934.0	0.0	12.69
2016-01-09 17:30:00	8.340	8.160	8.370	8.170	8.330	8.180	0.676	0.607	0.756	0.708	...	3.260	117.20	3.600	117.400	4.526	0.731	100.0	934.0	0.0	12.67
2016-01-09 17:40:00	8.130	7.929	8.090	7.895	7.972	7.788	0.557	0.507	0.534	0.498	...	3.677	115.90	3.371	115.900	3.515	0.852	100.0	933.0	0.0	12.68
2016-01-09 17:50:00	7.480	7.283	7.706	7.486	7.649	7.481	0.588	0.526	0.590	0.529	...	3.500	119.90	3.265	118.900	3.322	0.771	100.0	933.0	0.0	12.67
2016-01-09 18:00:00	7.554	7.452	7.484	7.359	7.578	7.456	0.734	0.681	0.631	0.573	...	3.061	113.90	2.663	111.400	2.789	0.913	100.0	933.0	0.0	12.65
2016-01-09 18:10:00	8.220	8.070	8.080	7.888	7.791	7.639	0.743	0.724	0.800	0.757	...	3.427	115.10	4.055	113.800	4.834	0.832	100.0	933.0	0.0	12.62
2016-01-09 18:20:00	9.420	9.270	9.660	9.470	9.570	9.450	0.482	0.438	0.566	0.522	...	2.431	119.50	2.454	117.400	3.017	0.549	100.0	933.0	0.0	12.61
2016-01-09 18:30:00	9.910	9.810	9.610	9.480	8.940	8.840	0.568	0.513	0.698	0.648	...	3.272	114.90	3.271	113.200	4.454	0.529	100.0	933.0	0.0	12.60
2016-01-09 18:40:00	10.390	10.340	10.260	10.110	9.840	9.760	0.683	0.640	0.710	0.696	...	4.405	110.90	4.308	109.300	4.776	0.589	100.0	933.0	0.0	12.60
2016-01-09 18:50:00	10.360	10.320	10.320	10.230	9.830	9.820	0.840	0.780	0.857	0.841	...	3.509	108.30	3.642	106.000	4.478	0.448	100.0	932.0	0.0	12.61
2016-01-09 19:00:00	9.950	9.880	10.050	9.970	9.910	9.870	0.561	0.469	0.639	0.547	...	3.601	102.30	3.085	100.700	3.954	0.701	100.0	932.0	0.0	12.60
2016-01-09 19:10:00	9.620	9.600	9.590	9.550	9.240	9.260	0.767	0.666	0.847	0.732	...	4.701	100.80	4.184	99.900	4.012	0.691	100.0	932.0	0.0	12.59
2016-01-09 19:20:00	9.590	9.600	9.510	9.460	9.270	9.270	0.615	0.571	0.795	0.760	...	5.744	109.30	5.838	105.200	5.478	0.711	100.0	932.0	0.0	12.58
2016-01-09 19:30:00	10.520	10.520	10.520	10.440	10.510	10.490	0.608	0.571	0.501	0.477	...	2.017	122.00	2.219	118.600	2.905	0.823	100.0	932.0	0.0	12.58
2016-01-09 19:40:00	8.970	8.950	9.190	9.100	9.260	9.190	0.691	0.617	0.705	0.659	...	3.884	123.10	4.036	119.200	3.911	0.913	100.0	931.0	0.0	12.57
2016-01-09 19:50:00	7.334	7.339	7.212	7.151	6.995	6.964	1.286	1.283	1.470	1.423	...	13.060	113.80	12.850	112.800	9.380	1.156	100.0	931.0	0.0	12.57
2016-01-09 20:00:00	5.310	5.445	4.788	4.901	4.093	4.190	1.202	1.190	1.259	1.226	...	16.890	92.30	16.320	82.100	20.130	1.317	100.0	931.0	0.0	12.56
2016-01-09 20:10:00	4.052	4.044	3.956	3.921	3.572	3.500	0.881	0.868	0.802	0.787	...	16.450	106.00	18.110	98.800	18.040	1.389	100.0	931.0	0.0	12.56
2016-01-09 20:20:00	3.632	3.519	3.653	3.584	3.594	3.524	0.816	0.787	0.658	0.615	...	11.660	95.00	10.780	90.900	10.660	1.288	100.0	931.0	0.0	12.55
2016-01-09 20:30:00	6.868	6.797	6.667	6.566	6.363	6.277	1.297	1.313	1.383	1.371	...	7.568	112.40	5.917	111.900	6.342	1.449	100.0	931.0	0.0	12.54
2016-01-09 20:40:00	3.496	3.462	4.098	4.110	4.252	4.318	1.363	1.367	1.616	1.593	...	13.770	104.30	12.860	106.200	12.810	1.317	100.0	931.0	0.0	12.54
2016-01-09 20:50:00	2.390	2.394	2.033	2.000	1.402	1.347	0.774	0.706	0.725	0.682	...	21.640	98.70	33.320	78.950	39.480	0.913	100.0	931.0	0.0	12.54
2016-01-09 21:00:00	3.002	3.042	2.509	2.521	2.252	2.156	1.313	1.265	1.237	1.285	...	22.610	73.82	25.350	56.030	33.450	1.338	100.0	931.0	0.0	12.53
2016-01-09 21:10:00	3.489	3.448	3.061	3.031	2.874	2.851	0.821	0.793	0.632	0.575	...	10.750	74.83	11.530	66.910	12.570	1.176	100.0	930.0	0.0	12.53
2016-01-09 21:20:00	4.204	4.321	3.661	3.715	2.972	3.074	0.995	0.924	1.049	1.005	...	10.920	83.60	11.370	75.780	14.990	1.236	100.0	930.0	0.0	12.52
2016-01-09 21:30:00	6.486	6.648	5.745	5.879	4.862	5.021	1.224	1.237	1.329	1.329	...	12.640	69.43	12.760	65.760	14.290	1.398	100.0	930.0	0.0	12.52
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2017-01-01 19:00:00	16.830	16.960	15.790	15.740	15.260	15.190	2.109	2.121	2.213	2.158	...	5.864	275.20	0.000	24.220	7.789	0.246	100.0	975.0	0.0	13.07
2017-01-01 19:10:00	15.070	15.210	13.880	13.870	12.780	12.790	1.345	1.312	1.723	1.713	...	4.644	275.20	0.000	27.490	5.928	-0.097	100.0	975.0	0.0	13.07
2017-01-01 19:20:00	13.530	13.640	12.710	12.680	11.840	11.920	1.327	1.272	1.428	1.378	...	4.862	275.20	0.000	27.340	5.738	0.003	100.0	976.0	0.0	13.07
2017-01-01 19:30:00	12.600	12.710	11.800	11.820	10.760	10.850	1.125	1.076	1.192	1.185	...	3.664	275.20	0.000	26.710	5.446	0.124	100.0	976.0	0.0	13.06
2017-01-01 19:40:00	11.030	11.150	10.310	10.280	9.450	9.490	1.008	0.970	1.067	1.014	...	4.025	275.20	0.000	27.130	6.349	0.225	100.0	976.0	0.0	13.06
2017-01-01 19:50:00	10.970	11.020	10.240	10.140	9.640	9.510	0.948	0.885	1.090	1.073	...	4.460	275.20	0.000	24.730	6.135	0.398	100.0	976.0	0.0	13.06
2017-01-01 20:00:00	9.220	9.250	8.590	8.480	8.210	7.898	1.301	1.259	1.326	1.288	...	6.344	275.20	0.000	16.530	8.410	0.408	100.0	976.0	0.0	13.05
2017-01-01 20:10:00	9.690	9.710	8.730	8.660	8.060	8.100	1.108	1.063	1.034	0.977	...	8.260	275.20	0.000	17.780	10.360	0.427	100.0	976.0	0.1	13.05
2017-01-01 20:20:00	9.940	10.010	9.240	9.180	8.870	8.930	1.152	1.089	1.303	1.245	...	6.100	275.20	0.000	14.710	8.140	0.458	100.0	976.0	0.0	13.05
2017-01-01 20:30:00	10.100	10.230	9.210	9.080	8.550	8.640	1.333	1.276	1.181	1.104	...	6.533	275.20	0.000	16.060	9.860	0.418	99.5	977.0	0.0	13.04
2017-01-01 20:40:00	10.920	11.040	10.190	9.990	9.570	9.700	1.420	1.355	1.313	1.254	...	5.977	275.20	0.000	21.060	7.317	0.418	99.0	977.0	0.0	13.04
2017-01-01 20:50:00	12.210	12.330	11.380	11.230	10.640	10.800	1.564	1.521	1.655	1.649	...	4.017	275.20	0.000	25.220	5.931	0.317	98.2	977.0	0.0	13.04
2017-01-01 21:00:00	11.900	12.050	11.190	11.040	10.540	10.680	0.986	0.951	0.958	0.927	...	4.436	275.20	0.000	22.000	6.066	0.306	98.5	977.0	0.0	13.04
2017-01-01 21:10:00	12.170	12.250	11.370	11.190	10.690	10.770	1.042	0.981	1.160	1.114	...	4.543	275.20	0.000	20.370	5.186	0.427	98.3	977.0	0.0	13.03
2017-01-01 21:20:00	12.570	12.710	11.910	11.760	11.260	11.400	1.252	1.212	1.348	1.307	...	3.612	275.20	0.000	23.020	5.229	0.317	96.9	977.0	0.0	13.03
2017-01-01 21:30:00	13.050	13.210	12.230	12.110	11.360	11.470	1.018	0.962	1.157	1.113	...	3.574	275.20	0.000	23.330	5.919	0.367	97.1	977.0	0.0	13.03
2017-01-01 21:40:00	11.820	11.940	10.850	10.750	9.770	9.880	1.281	1.219	1.455	1.383	...	4.477	275.20	0.000	21.990	6.635	0.225	96.0	978.0	0.0	13.02
2017-01-01 21:50:00	11.620	11.720	10.630	10.480	9.530	9.670	1.060	1.015	1.348	1.283	...	4.358	275.20	0.000	19.960	6.322	0.043	95.7	978.0	0.0	13.02
2017-01-01 22:00:00	10.970	11.070	10.010	9.870	8.910	9.000	0.857	0.790	0.879	0.824	...	4.366	275.20	0.000	21.390	6.312	-0.087	95.8	978.0	0.0	13.02
2017-01-01 22:10:00	12.030	12.090	11.320	11.140	10.730	10.770	1.175	1.121	1.249	1.208	...	3.851	275.20	0.000	21.520	5.347	0.033	95.9	978.0	0.0	13.01
2017-01-01 22:20:00	11.800	11.870	11.230	11.070	10.570	10.610	0.928	0.861	0.928	0.875	...	3.375	275.20	0.000	21.520	5.000	-0.168	93.9	978.0	0.0	13.01
2017-01-01 22:30:00	11.400	11.490	10.820	10.640	10.170	10.200	0.818	0.774	0.709	0.656	...	3.046	275.20	0.000	22.250	4.233	-0.159	94.2	978.0	0.0	13.01
2017-01-01 22:40:00	11.830	11.950	11.280	11.120	10.530	10.590	0.746	0.694	0.754	0.692	...	3.629	275.20	0.000	23.010	4.384	-0.178	93.1	978.0	0.0	13.01
2017-01-01 22:50:00	11.460	11.590	11.000	10.840	10.180	10.240	0.728	0.703	0.852	0.800	...	2.977	275.20	0.000	19.010	5.281	-0.219	93.5	978.0	0.0	13.00
2017-01-01 23:00:00	10.080	10.160	9.430	9.320	8.580	8.610	0.967	0.953	1.049	1.014	...	4.395	275.20	0.000	12.240	9.010	-0.391	93.8	979.0	0.0	13.00
2017-01-01 23:10:00	10.050	10.130	9.220	9.110	8.070	8.100	0.726	0.683	0.762	0.721	...	3.568	275.20	0.000	15.050	7.453	-0.512	95.5	979.0	0.0	13.00
2017-01-01 23:20:00	11.060	11.150	10.180	10.100	9.140	9.160	0.770	0.750	0.854	0.826	...	2.853	275.20	0.000	12.760	6.748	-0.624	96.1	979.0	0.0	13.00
2017-01-01 23:30:00	10.290	10.380	9.490	9.420	8.470	8.480	0.591	0.554	0.824	0.759	...	3.826	275.20	0.000	11.380	7.505	-0.705	96.8	979.0	0.0	13.00
2017-01-01 23:40:00	9.260	9.310	8.310	8.230	7.212	7.255	0.632	0.565	0.797	0.798	...	6.536	275.20	0.000	9.160	8.400	-0.877	96.8	979.0	0.0	12.99
2017-01-01 23:50:00	8.420	8.450	7.486	7.338	6.611	6.610	0.795	0.753	0.834	0.880	...	5.014	275.20	0.000	7.084	14.140	-0.887	97.1	979.0	0.0	12.99

48763 rows × 29 columns

These operations can also be performed on a specific column:

data['Spd80mN']['2017-01-01':'2017-02-01']

Timestamp
2017-01-01 00:00:00     5.876
2017-01-01 00:10:00     5.911
2017-01-01 00:20:00     7.004
2017-01-01 00:30:00     7.079
2017-01-01 00:40:00     6.796
2017-01-01 00:50:00     8.380
2017-01-01 01:00:00     8.910
2017-01-01 01:10:00     8.490
2017-01-01 01:20:00     7.116
2017-01-01 01:30:00     8.060
2017-01-01 01:40:00     9.150
2017-01-01 01:50:00     7.837
2017-01-01 02:00:00     6.234
2017-01-01 02:10:00     5.896
2017-01-01 02:20:00     4.897
2017-01-01 02:30:00     4.830
2017-01-01 02:40:00     4.728
2017-01-01 02:50:00     4.207
2017-01-01 03:00:00     4.315
2017-01-01 03:10:00     5.976
2017-01-01 03:20:00     6.539
2017-01-01 03:30:00     7.523
2017-01-01 03:40:00     7.904
2017-01-01 03:50:00     7.621
2017-01-01 04:00:00     7.791
2017-01-01 04:10:00     8.190
2017-01-01 04:20:00     8.200
2017-01-01 04:30:00     8.290
2017-01-01 04:40:00     8.230
2017-01-01 04:50:00     9.600
                        ...
2017-02-01 19:00:00     9.980
2017-02-01 19:10:00     7.264
2017-02-01 19:20:00     8.210
2017-02-01 19:30:00    11.040
2017-02-01 19:40:00    12.240
2017-02-01 19:50:00    13.340
2017-02-01 20:00:00    14.150
2017-02-01 20:10:00    15.540
2017-02-01 20:20:00    14.870
2017-02-01 20:30:00    15.220
2017-02-01 20:40:00    14.510
2017-02-01 20:50:00    13.570
2017-02-01 21:00:00    13.200
2017-02-01 21:10:00    12.780
2017-02-01 21:20:00    13.230
2017-02-01 21:30:00    12.160
2017-02-01 21:40:00    11.400
2017-02-01 21:50:00    11.530
2017-02-01 22:00:00    11.940
2017-02-01 22:10:00    11.850
2017-02-01 22:20:00    10.240
2017-02-01 22:30:00    11.000
2017-02-01 22:40:00    11.900
2017-02-01 22:50:00    11.160
2017-02-01 23:00:00    10.580
2017-02-01 23:10:00    11.190
2017-02-01 23:20:00    11.580
2017-02-01 23:30:00    14.320
2017-02-01 23:40:00    12.870
2017-02-01 23:50:00    13.330
Name: Spd80mN, Length: 4608, dtype: float64

These ranges can then too be used within functions, such as monthly_means().

bw.monthly_means(data['Spd80mN']['2017-01-01':])

---

Step 3: Selecting Specific Entries

Specific entries in DataFrames and Series can be accessed both by their index and columns name, or by their position in the DataFrame or Series.

To select a specific entry by its columns name and index, i.e. the entry in the column Spd80mN at timestamp 2016-01-09 17:00:00, type:

data['Spd80mN']['2016-01-09 17:00:00']

7.652

To select a specific entry by its position in the DataFrame, i.e. the 3rd entry in the 1st column, use .iloc. When using Pandas, indexing starts at 0 for both columns and rows of DataFrames and Series. The index for the the 3rd entry in the 1st column would therefore be [2, 0]:

data.iloc[2,0]

7.652

There are many ways to slice and dice a pandas DataFrame making it a little confusing. These are just a few and are for most things all that are required.

---