Loading CSV Data From Files

Loading CSV data from files The pandas livrary provides built-in support for loading data in .csv format

import pandas as pd
df = pd.read_csv('data/weather.csv')
df
MinTemp MaxTemp Rainfall Evaporation Sunshine WindGustDir WindGustSpeed WindDir9am WindDir3pm WindSpeed9am ... Humidity3pm Pressure9am Pressure3pm Cloud9am Cloud3pm Temp9am Temp3pm RainToday RISK_MM RainTomorrow
0 8.0 24.3 0.0 3.4 6.3 NW 30.0 SW NW 6.0 ... 29 1019.7 1015.0 7 7 14.4 23.6 No 3.6 Yes
1 14.0 26.9 3.6 4.4 9.7 ENE 39.0 E W 4.0 ... 36 1012.4 1008.4 5 3 17.5 25.7 Yes 3.6 Yes
2 13.7 23.4 3.6 5.8 3.3 NW 85.0 N NNE 6.0 ... 69 1009.5 1007.2 8 7 15.4 20.2 Yes 39.8 Yes
3 13.3 15.5 39.8 7.2 9.1 NW 54.0 WNW W 30.0 ... 56 1005.5 1007.0 2 7 13.5 14.1 Yes 2.8 Yes
4 7.6 16.1 2.8 5.6 10.6 SSE 50.0 SSE ESE 20.0 ... 49 1018.3 1018.5 7 7 11.1 15.4 Yes 0.0 No
5 6.2 16.9 0.0 5.8 8.2 SE 44.0 SE E 20.0 ... 57 1023.8 1021.7 7 5 10.9 14.8 No 0.2 No
6 6.1 18.2 0.2 4.2 8.4 SE 43.0 SE ESE 19.0 ... 47 1024.6 1022.2 4 6 12.4 17.3 No 0.0 No
7 8.3 17.0 0.0 5.6 4.6 E 41.0 SE E 11.0 ... 57 1026.2 1024.2 6 7 12.1 15.5 No 0.0 No
8 8.8 19.5 0.0 4.0 4.1 S 48.0 E ENE 19.0 ... 48 1026.1 1022.7 7 7 14.1 18.9 No 16.2 Yes
9 8.4 22.8 16.2 5.4 7.7 E 31.0 S ESE 7.0 ... 32 1024.1 1020.7 7 1 13.3 21.7 Yes 0.0 No
10 9.1 25.2 0.0 4.2 11.9 N 30.0 SE NW 6.0 ... 34 1024.4 1021.1 1 2 14.6 24.0 No 0.2 No
11 8.5 27.3 0.2 7.2 12.5 E 41.0 E NW 2.0 ... 35 1023.8 1019.9 0 3 16.8 26.0 No 0.0 No
12 10.1 27.9 0.0 7.2 13.0 WNW 30.0 S NW 6.0 ... 29 1022.0 1017.1 0 1 17.0 27.1 No 0.0 No
13 12.1 30.9 0.0 6.2 12.4 NW 44.0 WNW W 7.0 ... 20 1017.3 1013.1 1 4 19.7 30.7 No 0.0 No
14 10.1 31.2 0.0 8.8 13.1 NW 41.0 S W 6.0 ... 16 1018.2 1013.7 0 1 18.7 30.4 No 0.0 No
15 12.4 32.1 0.0 8.4 11.1 E 46.0 SE WSW 7.0 ... 22 1017.9 1012.8 0 3 19.1 30.7 No 0.0 No
16 13.8 31.2 0.0 7.2 8.4 ESE 44.0 WSW W 6.0 ... 23 1014.4 1009.8 7 6 20.2 29.8 No 1.2 Yes
17 11.7 30.0 1.2 7.2 10.1 S 52.0 SW NE 6.0 ... 26 1016.4 1013.0 1 5 20.1 28.6 Yes 0.6 No
18 12.4 32.3 0.6 7.4 13.0 E 39.0 NNE W 4.0 ... 25 1017.1 1013.3 1 3 20.2 31.2 No 0.0 No
19 15.6 33.4 0.0 8.0 10.4 NE 33.0 NNW NNW 2.0 ... 27 1018.5 1013.7 0 1 22.8 32.0 No 0.0 No
20 15.3 33.4 0.0 8.8 9.5 WNW 59.0 N NW 2.0 ... 26 1012.4 1006.5 1 5 22.2 32.8 No 0.4 No
21 16.4 19.4 0.4 9.2 0.0 E 26.0 ENE E 6.0 ... 72 1010.7 1008.9 8 8 16.5 18.3 No 25.8 Yes
22 12.8 18.5 25.8 2.8 0.6 ESE 28.0 S SE 13.0 ... 79 1014.0 1014.9 8 8 14.0 16.8 Yes 0.4 No
23 12.0 24.3 0.4 1.2 7.5 NNE 26.0 WSW NE 6.0 ... 57 1020.7 1019.2 7 5 17.8 22.8 No 0.0 No
24 15.4 28.4 0.0 4.4 8.1 ENE 33.0 SSE NE 9.0 ... 31 1022.4 1018.6 8 2 16.8 27.3 No 0.0 No
25 15.6 26.9 0.0 6.8 8.9 E 41.0 E E 6.0 ... 48 1019.7 1016.5 2 4 19.8 25.1 No 0.2 No
26 13.3 22.2 0.2 6.6 2.3 ENE 39.0 E E 20.0 ... 55 1021.0 1018.6 7 7 16.5 21.2 No 0.0 No
27 12.9 28.0 0.0 4.4 10.7 S 52.0 S NNE 6.0 ... 31 1019.2 1014.8 5 7 18.8 26.7 No 0.0 No
28 15.1 24.3 0.0 7.0 0.4 SE 39.0 SE SE 7.0 ... 80 1019.0 1017.1 7 7 18.9 19.7 No 0.4 No
29 13.6 24.1 0.4 2.6 0.5 NNW 30.0 SSW S 6.0 ... 49 1017.2 1013.3 8 7 17.3 23.2 No 22.6 Yes
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
336 5.6 27.6 0.0 5.2 11.0 NW 46.0 NNW NW 15.0 ... 21 1017.7 1014.1 0 0 19.0 26.7 No 0.0 No
337 16.8 28.9 0.0 10.0 10.8 NNW 70.0 NW NW 31.0 ... 22 1016.3 1011.8 1 1 22.5 28.4 No 7.6 Yes
338 14.4 20.7 7.6 9.4 4.9 NNW 33.0 NNW NNW 20.0 ... 65 1015.5 1013.2 8 4 14.5 19.3 Yes 3.0 Yes
339 10.3 21.3 3.0 4.2 6.7 NNW 43.0 ENE N 7.0 ... 46 1018.1 1013.6 8 1 11.7 19.8 Yes 0.0 No
340 11.2 18.0 0.0 4.8 8.4 W 65.0 N W 24.0 ... 40 1009.5 1005.3 5 4 12.8 16.2 No 8.2 Yes
341 0.3 16.0 8.2 5.4 11.8 NW 57.0 NNW N 11.0 ... 45 1016.8 1013.3 1 1 6.9 14.6 Yes 0.0 No
342 0.5 17.9 0.0 5.8 11.5 N 44.0 NNE NNW 2.0 ... 33 1019.1 1017.5 0 1 7.2 16.6 No 0.0 No
343 0.5 20.0 0.0 6.2 11.5 NNW 31.0 S N 2.0 ... 22 1026.2 1024.2 0 1 8.1 18.8 No 0.0 No
344 4.6 22.0 0.0 4.4 11.0 N 41.0 NNW N 6.0 ... 25 1028.8 1024.9 1 2 10.0 21.4 No 0.0 No
345 8.2 22.4 0.0 5.4 11.2 NW 31.0 SSW NW 2.0 ... 30 1027.8 1023.8 1 3 13.6 20.6 No 0.0 No
346 4.5 23.9 0.0 4.8 11.7 NW 30.0 ENE NNW 4.0 ... 27 1025.8 1021.5 0 4 12.6 22.3 No 0.0 No
347 6.7 26.1 0.0 6.2 7.5 SSW 70.0 NE NNW 6.0 ... 47 1020.9 1016.0 4 7 16.3 23.2 No 13.2 Yes
348 11.9 21.1 13.2 6.6 NaN NW 41.0 NNE N 7.0 ... 61 1019.2 1016.7 7 3 14.5 19.4 Yes 0.6 No
349 9.2 19.6 0.6 3.4 10.4 ENE 31.0 SSE NNW 4.0 ... 42 1022.3 1019.7 7 4 11.6 18.4 No 0.0 No
350 4.4 21.0 0.0 4.2 12.2 NW 28.0 SW NW 2.0 ... 30 1025.7 1022.3 1 1 9.6 19.2 No 0.0 No
351 5.0 24.1 0.0 6.2 12.0 NNW 52.0 NaN NNW 0.0 ... 34 1024.5 1020.7 6 1 11.6 21.9 No 0.0 No
352 6.7 24.7 0.0 5.4 8.6 NW 43.0 N NW 4.0 ... 31 1025.7 1022.2 1 7 12.7 23.7 No 0.0 No
353 8.3 28.5 0.0 5.8 9.8 NW 46.0 W NW 2.0 ... 30 1024.1 1019.8 1 6 16.8 27.4 No 0.2 No
354 11.3 27.4 0.2 7.6 12.1 NW 52.0 SE NW 6.0 ... 20 1021.4 1017.5 1 1 16.4 26.3 No 0.0 No
355 9.0 20.6 0.0 9.0 6.2 ENE 39.0 S SW 11.0 ... 28 1022.3 1018.6 7 5 11.4 18.5 No 0.8 No
356 3.4 15.0 0.8 4.8 11.7 S 70.0 S S 35.0 ... 24 1023.4 1023.1 1 5 8.3 14.3 No 0.0 No
357 3.2 18.0 0.0 7.4 12.2 SSE 48.0 SSE S 26.0 ... 25 1026.6 1022.8 1 2 9.1 16.3 No 0.0 No
358 0.9 20.7 0.0 5.4 8.4 NNW 39.0 SSE N 2.0 ... 29 1023.2 1018.4 3 8 9.4 19.1 No 0.0 No
359 3.3 25.5 0.0 5.2 10.8 N 43.0 N NNW 4.0 ... 16 1018.8 1014.6 0 3 12.0 24.8 No 0.0 No
360 7.9 26.1 0.0 6.8 3.5 NNW 43.0 NaN WNW 0.0 ... 20 1017.6 1014.2 5 8 16.3 25.9 No 0.0 No
361 9.0 30.7 0.0 7.6 12.1 NNW 76.0 SSE NW 7.0 ... 15 1016.1 1010.8 1 3 20.4 30.0 No 0.0 No
362 7.1 28.4 0.0 11.6 12.7 N 48.0 NNW NNW 2.0 ... 22 1020.0 1016.9 0 1 17.2 28.2 No 0.0 No
363 12.5 19.9 0.0 8.4 5.3 ESE 43.0 ENE ENE 11.0 ... 47 1024.0 1022.8 3 2 14.5 18.3 No 0.0 No
364 12.5 26.9 0.0 5.0 7.1 NW 46.0 SSW WNW 6.0 ... 39 1021.0 1016.2 6 7 15.8 25.9 No 0.0 No
365 12.3 30.2 0.0 6.0 12.6 NW 78.0 NW WNW 31.0 ... 13 1009.6 1009.2 1 1 23.8 28.6 No 0.0 No

366 rows × 22 columns

# the contents of the MinTemp column
df.MinTemp
0       8.0
1      14.0
2      13.7
3      13.3
4       7.6
5       6.2
6       6.1
7       8.3
8       8.8
9       8.4
10      9.1
11      8.5
12     10.1
13     12.1
14     10.1
15     12.4
16     13.8
17     11.7
18     12.4
19     15.6
20     15.3
21     16.4
22     12.8
23     12.0
24     15.4
25     15.6
26     13.3
27     12.9
28     15.1
29     13.6
       ... 
336     5.6
337    16.8
338    14.4
339    10.3
340    11.2
341     0.3
342     0.5
343     0.5
344     4.6
345     8.2
346     4.5
347     6.7
348    11.9
349     9.2
350     4.4
351     5.0
352     6.7
353     8.3
354    11.3
355     9.0
356     3.4
357     3.2
358     0.9
359     3.3
360     7.9
361     9.0
362     7.1
363    12.5
364    12.5
365    12.3
Name: MinTemp, Length: 366, dtype: float64

# we can get the first value in the MinTemp column
df.MinTemp[0]
8.0

# it is a float
type(df.MinTemp[0])
numpy.float64