hidrokit.viz.table
¶taruma_hidrokit_viz_table
1.0.1
/20190713
0.2.0
3.7
### Instalasi melalui PyPI
!pip install hidrokit
### Instalasi melalui Github
# !pip install git+https://github.com/taruma/hidrokit.git
### Instalasi melalui Github (Latest)
# !pip install git+https://github.com/taruma/hidrokit.git@latest
Collecting hidrokit Downloading https://files.pythonhosted.org/packages/43/9d/343d2a413a07463a21dd13369e31d664d6733bbfd46276abef5d804c83d1/hidrokit-0.2.0-py2.py3-none-any.whl Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from hidrokit) (3.0.3) Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from hidrokit) (1.16.4) Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from hidrokit) (0.24.2) Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->hidrokit) (2.5.3) Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->hidrokit) (1.1.0) Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->hidrokit) (2.4.0) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->hidrokit) (0.10.0) Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->hidrokit) (2018.9) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib->hidrokit) (1.12.0) Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from kiwisolver>=1.0.1->matplotlib->hidrokit) (41.0.1) Installing collected packages: hidrokit Successfully installed hidrokit-0.2.0
import numpy as np
import pandas as pd
# Ambil dataset dari data test hidrokit
!wget -O dataset.csv "https://github.com/taruma/hidrokit/blob/master/tests/data/one_year_three_columns.csv?raw=true"
--2019-07-12 03:02:01-- https://github.com/taruma/hidrokit/blob/master/tests/data/one_year_three_columns.csv?raw=true Resolving github.com (github.com)... 192.30.253.112 Connecting to github.com (github.com)|192.30.253.112|:443... connected. HTTP request sent, awaiting response... 302 Found Location: https://github.com/taruma/hidrokit/raw/master/tests/data/one_year_three_columns.csv [following] --2019-07-12 03:02:01-- https://github.com/taruma/hidrokit/raw/master/tests/data/one_year_three_columns.csv Reusing existing connection to github.com:443. HTTP request sent, awaiting response... 302 Found Location: https://raw.githubusercontent.com/taruma/hidrokit/master/tests/data/one_year_three_columns.csv [following] --2019-07-12 03:02:01-- https://raw.githubusercontent.com/taruma/hidrokit/master/tests/data/one_year_three_columns.csv Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ... Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 7242 (7.1K) [text/plain] Saving to: ‘dataset.csv’ dataset.csv 100%[===================>] 7.07K --.-KB/s in 0s 2019-07-12 03:02:01 (90.8 MB/s) - ‘dataset.csv’ saved [7242/7242]
# Baca dataset
dataset = pd.read_csv('dataset.csv', index_col=0, parse_dates=True)
dataset.head(10)
sta_a | sta_b | sta_c | |
---|---|---|---|
2000-01-01 | 7 | 79 | 19 |
2000-01-02 | 17 | 79 | 65 |
2000-01-03 | 79 | 51 | 25 |
2000-01-04 | 48 | 75 | 31 |
2000-01-05 | 81 | 33 | 80 |
2000-01-06 | 26 | 3 | 96 |
2000-01-07 | 78 | 75 | 26 |
2000-01-08 | 71 | 95 | 65 |
2000-01-09 | 48 | 71 | 22 |
2000-01-10 | 32 | 89 | 88 |
# Info dataset
dataset.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 366 entries, 2000-01-01 to 2000-12-31 Data columns (total 3 columns): sta_a 366 non-null int64 sta_b 366 non-null int64 sta_c 366 non-null int64 dtypes: int64(3) memory usage: 11.4 KB
table.pivot()
¶viz.table.pivot(dataframe, column=None, lang=None)
DataFrame
from hidrokit.viz import table
Jika tidak ada kolom yang dipilih, maka akan dipilih kolom pertama (sta_a
).
table.pivot(dataset)
month | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
day | ||||||||||||
1 | 7.0 | 49.0 | 55.0 | 82.0 | 18.0 | 61.0 | 54.0 | 5.0 | 14.0 | 27.0 | 59.0 | 28.0 |
2 | 17.0 | 76.0 | 6.0 | 94.0 | 86.0 | 92.0 | 27.0 | 19.0 | 77.0 | 70.0 | 65.0 | 95.0 |
3 | 79.0 | 17.0 | 95.0 | 3.0 | 79.0 | 69.0 | 68.0 | 41.0 | 48.0 | 83.0 | 88.0 | 86.0 |
4 | 48.0 | 43.0 | 67.0 | 34.0 | 41.0 | 24.0 | 58.0 | 25.0 | 94.0 | 96.0 | 31.0 | 12.0 |
5 | 81.0 | 38.0 | 41.0 | 62.0 | 5.0 | 70.0 | 67.0 | 27.0 | 61.0 | 5.0 | 96.0 | 84.0 |
6 | 26.0 | 25.0 | 16.0 | 20.0 | 19.0 | 53.0 | 60.0 | 40.0 | 1.0 | 75.0 | 62.0 | 43.0 |
7 | 78.0 | 91.0 | 88.0 | 31.0 | 66.0 | 27.0 | 35.0 | 98.0 | 64.0 | 31.0 | 10.0 | 13.0 |
8 | 71.0 | 58.0 | 46.0 | 85.0 | 20.0 | 42.0 | 39.0 | 22.0 | 64.0 | 10.0 | 46.0 | 23.0 |
9 | 48.0 | 8.0 | 7.0 | 54.0 | 26.0 | 38.0 | 67.0 | 6.0 | 11.0 | 9.0 | 82.0 | 9.0 |
10 | 32.0 | 58.0 | 73.0 | 61.0 | 91.0 | 86.0 | 18.0 | 22.0 | 12.0 | 98.0 | 40.0 | 94.0 |
11 | 66.0 | 94.0 | 62.0 | 80.0 | 99.0 | 7.0 | 12.0 | 36.0 | 24.0 | 62.0 | 54.0 | 44.0 |
12 | 93.0 | 62.0 | 18.0 | 37.0 | 94.0 | 47.0 | 41.0 | 38.0 | 3.0 | 0.0 | 59.0 | 43.0 |
13 | 94.0 | 64.0 | 37.0 | 60.0 | 80.0 | 91.0 | 29.0 | 39.0 | 61.0 | 93.0 | 64.0 | 12.0 |
14 | 98.0 | 80.0 | 80.0 | 89.0 | 90.0 | 58.0 | 58.0 | 93.0 | 10.0 | 17.0 | 35.0 | 96.0 |
15 | 40.0 | 85.0 | 97.0 | 12.0 | 1.0 | 48.0 | 76.0 | 75.0 | 7.0 | 9.0 | 42.0 | 74.0 |
16 | 46.0 | 1.0 | 0.0 | 86.0 | 18.0 | 96.0 | 70.0 | 3.0 | 28.0 | 11.0 | 93.0 | 31.0 |
17 | 34.0 | 21.0 | 73.0 | 58.0 | 26.0 | 97.0 | 3.0 | 24.0 | 96.0 | 23.0 | 35.0 | 22.0 |
18 | 21.0 | 36.0 | 22.0 | 5.0 | 60.0 | 84.0 | 75.0 | 29.0 | 39.0 | 76.0 | 47.0 | 33.0 |
19 | 61.0 | 40.0 | 60.0 | 28.0 | 65.0 | 20.0 | 68.0 | 18.0 | 50.0 | 12.0 | 37.0 | 5.0 |
20 | 62.0 | 6.0 | 47.0 | 91.0 | 28.0 | 60.0 | 25.0 | 51.0 | 63.0 | 34.0 | 70.0 | 88.0 |
21 | 25.0 | 73.0 | 11.0 | 66.0 | 50.0 | 33.0 | 18.0 | 74.0 | 67.0 | 63.0 | 45.0 | 2.0 |
22 | 98.0 | 18.0 | 87.0 | 22.0 | 86.0 | 24.0 | 45.0 | 93.0 | 19.0 | 38.0 | 67.0 | 80.0 |
23 | 51.0 | 25.0 | 12.0 | 68.0 | 31.0 | 49.0 | 43.0 | 32.0 | 67.0 | 75.0 | 49.0 | 57.0 |
24 | 83.0 | 58.0 | 30.0 | 20.0 | 81.0 | 85.0 | 10.0 | 95.0 | 94.0 | 72.0 | 80.0 | 58.0 |
25 | 73.0 | 47.0 | 74.0 | 72.0 | 1.0 | 44.0 | 72.0 | 54.0 | 61.0 | 91.0 | 21.0 | 2.0 |
26 | 54.0 | 87.0 | 13.0 | 25.0 | 72.0 | 30.0 | 19.0 | 11.0 | 63.0 | 33.0 | 70.0 | 84.0 |
27 | 90.0 | 38.0 | 10.0 | 72.0 | 60.0 | 49.0 | 1.0 | 65.0 | 72.0 | 76.0 | 83.0 | 0.0 |
28 | 61.0 | 81.0 | 99.0 | 19.0 | 11.0 | 44.0 | 82.0 | 41.0 | 50.0 | 93.0 | 12.0 | 65.0 |
29 | 6.0 | 69.0 | 92.0 | 26.0 | 3.0 | 99.0 | 40.0 | 71.0 | 85.0 | 75.0 | 31.0 | 35.0 |
30 | 64.0 | NaN | 26.0 | 35.0 | 92.0 | 55.0 | 65.0 | 33.0 | 84.0 | 40.0 | 82.0 | 24.0 |
31 | 72.0 | NaN | 37.0 | NaN | 22.0 | NaN | 86.0 | 24.0 | NaN | 31.0 | NaN | 95.0 |
column=
¶Memilih kolom tertentu.
# Memilih stasiun b
table.pivot(dataset, column='sta_b')
month | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
---|---|---|---|---|---|---|---|---|---|---|---|---|
day | ||||||||||||
1 | 79.0 | 14.0 | 47.0 | 11.0 | 38.0 | 24.0 | 91.0 | 74.0 | 11.0 | 67.0 | 67.0 | 5.0 |
2 | 79.0 | 21.0 | 4.0 | 28.0 | 35.0 | 95.0 | 64.0 | 34.0 | 76.0 | 28.0 | 68.0 | 26.0 |
3 | 51.0 | 65.0 | 35.0 | 90.0 | 86.0 | 29.0 | 97.0 | 0.0 | 61.0 | 0.0 | 59.0 | 27.0 |
4 | 75.0 | 47.0 | 74.0 | 62.0 | 75.0 | 32.0 | 47.0 | 96.0 | 77.0 | 57.0 | 4.0 | 58.0 |
5 | 33.0 | 48.0 | 90.0 | 23.0 | 11.0 | 75.0 | 69.0 | 1.0 | 31.0 | 84.0 | 86.0 | 57.0 |
6 | 3.0 | 63.0 | 50.0 | 6.0 | 71.0 | 29.0 | 73.0 | 73.0 | 43.0 | 45.0 | 65.0 | 69.0 |
7 | 75.0 | 85.0 | 6.0 | 42.0 | 68.0 | 51.0 | 38.0 | 57.0 | 79.0 | 9.0 | 88.0 | 76.0 |
8 | 95.0 | 81.0 | 83.0 | 30.0 | 25.0 | 21.0 | 68.0 | 90.0 | 99.0 | 1.0 | 23.0 | 63.0 |
9 | 71.0 | 48.0 | 28.0 | 13.0 | 44.0 | 76.0 | 51.0 | 58.0 | 95.0 | 18.0 | 31.0 | 50.0 |
10 | 89.0 | 23.0 | 32.0 | 47.0 | 61.0 | 66.0 | 37.0 | 34.0 | 10.0 | 67.0 | 1.0 | 94.0 |
11 | 63.0 | 90.0 | 28.0 | 73.0 | 58.0 | 47.0 | 3.0 | 93.0 | 38.0 | 32.0 | 79.0 | 90.0 |
12 | 41.0 | 58.0 | 13.0 | 58.0 | 66.0 | 3.0 | 54.0 | 8.0 | 45.0 | 46.0 | 75.0 | 12.0 |
13 | 0.0 | 3.0 | 29.0 | 44.0 | 99.0 | 67.0 | 34.0 | 84.0 | 88.0 | 8.0 | 50.0 | 60.0 |
14 | 15.0 | 31.0 | 5.0 | 95.0 | 24.0 | 83.0 | 86.0 | 78.0 | 99.0 | 34.0 | 83.0 | 75.0 |
15 | 73.0 | 44.0 | 4.0 | 22.0 | 45.0 | 51.0 | 3.0 | 27.0 | 34.0 | 41.0 | 22.0 | 53.0 |
16 | 22.0 | 39.0 | 23.0 | 51.0 | 74.0 | 75.0 | 83.0 | 63.0 | 56.0 | 62.0 | 14.0 | 54.0 |
17 | 33.0 | 70.0 | 52.0 | 16.0 | 52.0 | 71.0 | 16.0 | 71.0 | 91.0 | 20.0 | 35.0 | 17.0 |
18 | 38.0 | 90.0 | 93.0 | 16.0 | 87.0 | 95.0 | 72.0 | 85.0 | 80.0 | 1.0 | 68.0 | 56.0 |
19 | 50.0 | 69.0 | 73.0 | 13.0 | 95.0 | 44.0 | 46.0 | 77.0 | 75.0 | 42.0 | 52.0 | 67.0 |
20 | 45.0 | 66.0 | 3.0 | 93.0 | 67.0 | 67.0 | 17.0 | 47.0 | 34.0 | 46.0 | 42.0 | 17.0 |
21 | 40.0 | 73.0 | 77.0 | 28.0 | 57.0 | 78.0 | 27.0 | 65.0 | 50.0 | 99.0 | 56.0 | 0.0 |
22 | 76.0 | 29.0 | 50.0 | 32.0 | 8.0 | 64.0 | 63.0 | 67.0 | 73.0 | 21.0 | 77.0 | 32.0 |
23 | 7.0 | 9.0 | 15.0 | 41.0 | 69.0 | 51.0 | 41.0 | 96.0 | 0.0 | 1.0 | 81.0 | 80.0 |
24 | 12.0 | 72.0 | 17.0 | 39.0 | 64.0 | 60.0 | 99.0 | 14.0 | 10.0 | 23.0 | 2.0 | 88.0 |
25 | 55.0 | 97.0 | 25.0 | 70.0 | 42.0 | 64.0 | 65.0 | 25.0 | 62.0 | 89.0 | 3.0 | 93.0 |
26 | 40.0 | 22.0 | 72.0 | 88.0 | 32.0 | 68.0 | 81.0 | 59.0 | 86.0 | 30.0 | 57.0 | 76.0 |
27 | 40.0 | 71.0 | 45.0 | 14.0 | 65.0 | 82.0 | 7.0 | 1.0 | 11.0 | 49.0 | 16.0 | 62.0 |
28 | 32.0 | 98.0 | 75.0 | 44.0 | 61.0 | 13.0 | 95.0 | 44.0 | 74.0 | 5.0 | 47.0 | 17.0 |
29 | 75.0 | 79.0 | 5.0 | 19.0 | 72.0 | 5.0 | 34.0 | 13.0 | 1.0 | 6.0 | 7.0 | 96.0 |
30 | 51.0 | NaN | 67.0 | 74.0 | 54.0 | 58.0 | 42.0 | 75.0 | 58.0 | 13.0 | 84.0 | 17.0 |
31 | 75.0 | NaN | 50.0 | NaN | 77.0 | NaN | 1.0 | 14.0 | NaN | 25.0 | NaN | 31.0 |
lang=
¶Memberi nama bulan pada kolom. Ada dua pilihan yaitu kode bahasa indonesia id
dan bahasa inggris en
.
# Menampilkan data stasiun c dengan nama bulan
table.pivot(dataset, column='sta_c', lang='en')
month | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |
---|---|---|---|---|---|---|---|---|---|---|---|---|
day | ||||||||||||
1 | 19.0 | 8.0 | 8.0 | 75.0 | 56.0 | 34.0 | 66.0 | 18.0 | 41.0 | 41.0 | 83.0 | 10.0 |
2 | 65.0 | 36.0 | 42.0 | 96.0 | 28.0 | 40.0 | 54.0 | 53.0 | 75.0 | 31.0 | 46.0 | 59.0 |
3 | 25.0 | 10.0 | 57.0 | 67.0 | 22.0 | 41.0 | 76.0 | 59.0 | 89.0 | 44.0 | 16.0 | 75.0 |
4 | 31.0 | 54.0 | 81.0 | 0.0 | 42.0 | 69.0 | 5.0 | 27.0 | 21.0 | 4.0 | 61.0 | 17.0 |
5 | 80.0 | 60.0 | 51.0 | 96.0 | 84.0 | 77.0 | 74.0 | 35.0 | 43.0 | 46.0 | 26.0 | 55.0 |
6 | 96.0 | 21.0 | 57.0 | 97.0 | 23.0 | 98.0 | 29.0 | 23.0 | 77.0 | 52.0 | 15.0 | 42.0 |
7 | 26.0 | 57.0 | 49.0 | 46.0 | 65.0 | 35.0 | 31.0 | 43.0 | 22.0 | 18.0 | 16.0 | 4.0 |
8 | 65.0 | 93.0 | 98.0 | 11.0 | 74.0 | 89.0 | 71.0 | 68.0 | 76.0 | 32.0 | 13.0 | 20.0 |
9 | 22.0 | 96.0 | 22.0 | 15.0 | 95.0 | 83.0 | 16.0 | 83.0 | 54.0 | 61.0 | 68.0 | 65.0 |
10 | 88.0 | 68.0 | 60.0 | 75.0 | 45.0 | 31.0 | 17.0 | 1.0 | 68.0 | 86.0 | 26.0 | 48.0 |
11 | 55.0 | 52.0 | 87.0 | 65.0 | 67.0 | 90.0 | 20.0 | 93.0 | 72.0 | 54.0 | 36.0 | 84.0 |
12 | 80.0 | 44.0 | 77.0 | 98.0 | 82.0 | 36.0 | 59.0 | 66.0 | 72.0 | 55.0 | 23.0 | 38.0 |
13 | 68.0 | 36.0 | 67.0 | 61.0 | 89.0 | 22.0 | 39.0 | 42.0 | 10.0 | 86.0 | 11.0 | 39.0 |
14 | 90.0 | 79.0 | 30.0 | 86.0 | 29.0 | 61.0 | 43.0 | 58.0 | 63.0 | 0.0 | 72.0 | 97.0 |
15 | 73.0 | 10.0 | 1.0 | 79.0 | 86.0 | 92.0 | 25.0 | 35.0 | 97.0 | 47.0 | 26.0 | 40.0 |
16 | 12.0 | 47.0 | 0.0 | 92.0 | 94.0 | 3.0 | 89.0 | 81.0 | 58.0 | 67.0 | 65.0 | 57.0 |
17 | 72.0 | 63.0 | 31.0 | 85.0 | 65.0 | 43.0 | 86.0 | 72.0 | 38.0 | 44.0 | 73.0 | 30.0 |
18 | 37.0 | 13.0 | 40.0 | 67.0 | 29.0 | 67.0 | 2.0 | 24.0 | 50.0 | 53.0 | 84.0 | 9.0 |
19 | 45.0 | 20.0 | 52.0 | 31.0 | 89.0 | 41.0 | 29.0 | 28.0 | 47.0 | 16.0 | 49.0 | 82.0 |
20 | 92.0 | 2.0 | 51.0 | 33.0 | 63.0 | 20.0 | 89.0 | 36.0 | 75.0 | 29.0 | 50.0 | 41.0 |
21 | 4.0 | 49.0 | 26.0 | 98.0 | 41.0 | 65.0 | 77.0 | 19.0 | 15.0 | 49.0 | 22.0 | 52.0 |
22 | 12.0 | 49.0 | 33.0 | 60.0 | 8.0 | 59.0 | 17.0 | 36.0 | 41.0 | 19.0 | 93.0 | 81.0 |
23 | 45.0 | 35.0 | 38.0 | 54.0 | 19.0 | 37.0 | 1.0 | 33.0 | 39.0 | 74.0 | 41.0 | 0.0 |
24 | 92.0 | 70.0 | 55.0 | 53.0 | 46.0 | 90.0 | 50.0 | 15.0 | 56.0 | 95.0 | 47.0 | 66.0 |
25 | 27.0 | 35.0 | 14.0 | 88.0 | 62.0 | 33.0 | 74.0 | 2.0 | 44.0 | 54.0 | 87.0 | 4.0 |
26 | 45.0 | 21.0 | 10.0 | 50.0 | 22.0 | 96.0 | 55.0 | 94.0 | 44.0 | 49.0 | 64.0 | 48.0 |
27 | 13.0 | 42.0 | 62.0 | 77.0 | 52.0 | 43.0 | 97.0 | 85.0 | 88.0 | 80.0 | 25.0 | 70.0 |
28 | 84.0 | 34.0 | 19.0 | 72.0 | 35.0 | 77.0 | 88.0 | 89.0 | 16.0 | 46.0 | 82.0 | 56.0 |
29 | 8.0 | 96.0 | 73.0 | 25.0 | 27.0 | 41.0 | 83.0 | 32.0 | 51.0 | 30.0 | 1.0 | 4.0 |
30 | 45.0 | NaN | 97.0 | 92.0 | 39.0 | 0.0 | 69.0 | 80.0 | 18.0 | 26.0 | 44.0 | 55.0 |
31 | 76.0 | NaN | 7.0 | NaN | 69.0 | NaN | 65.0 | 26.0 | NaN | 77.0 | NaN | 39.0 |
- 20190713 - 1.0.1 - Update information
- 20190712 - 1.0.0 - Initial
Source code in this notebook is licensed under a MIT License. Data in this notebook is licensed under a Creative Common Attribution 4.0 International.