EDA-02B: Visualisasi Data Runtun Waktu (Time-Series Visualizations)

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Code Lesson EDA-02-B

Code dari lesson ini dapat di akses di Link berikut (wajib login ke Google/Gmail): Code EDA-02-B
Di link tersebut anda langsung bisa merubah code dan menjalankannya. Keterangan lebih lanjut di video yang disertakan.
Sangat disarankan untuk membuka code dan video "side-by-side" untuk mendapatkan pengalaman belajar yang baik (Gambar dibawah). SIlahkan modifikasi (coba-coba) hal lain, selain yang ditunjukkan di video untuk mendapatkan pengalaman belajar yang lebih mendalam. Tentu saja juga silahkan akses berbagai referensi lain untuk memperkaya pengetahuan lalu diskusikan di forum yang telah disediakan.

"Side-by-Side": Ilustrasi bagaimana menggunakan code dan video dalam pembelajaran di tau-data. untuk mendapatkan pengalaman belajar yang baik.

tau-data Indonesia

Exploratory Data Analysis-02-B: Time Series Data Visualizations

(C) Taufik Sutanto

https://tau-data.id/eda-02b/ ~ taufik@tau-data.id

Outline¶

• String to DateTime
• DateTime Operations
• Time Series Visualizations

Catatan:¶

• Code & Video " akan " tersedia di https://tau-data.id/eda-02b/
• Mahasiswa dipersilahkan merekam kuliah untuk kepentingan pribadi (personal). Tidak untuk di upload/reshare.

DateTime Formats¶

DateTime Universal Codes¶

Very Good Reference: https://www.foragoodstrftime.com/¶

In [13]:

# Rubah Teks ke DateTime Format 
from datetime import datetime
# %a %b %d %l %m %M %z %Y https://www.foragoodstrftime.com/
d1 = datetime.strptime('Apr 28 2021  1:33PM', '%b %d %Y %I:%M%p')
d2 = datetime.strptime('Jun 1 2004  1:33PM', '%b %d %Y %I:%M%p')
d1

Out[13]:

datetime.datetime(2021, 4, 28, 13, 33)

In [4]:

print(dir(d1))

['__add__', '__class__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__ne__', '__new__', '__radd__', '__reduce__', '__reduce_ex__', '__repr__', '__rsub__', '__setattr__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', 'astimezone', 'combine', 'ctime', 'date', 'day', 'dst', 'fold', 'fromisocalendar', 'fromisoformat', 'fromordinal', 'fromtimestamp', 'hour', 'isocalendar', 'isoformat', 'isoweekday', 'max', 'microsecond', 'min', 'minute', 'month', 'now', 'replace', 'resolution', 'second', 'strftime', 'strptime', 'time', 'timestamp', 'timetuple', 'timetz', 'today', 'toordinal', 'tzinfo', 'tzname', 'utcfromtimestamp', 'utcnow', 'utcoffset', 'utctimetuple', 'weekday', 'year']

In [12]:

# weekday 0=Senin, 1=Selasa, 2=Rabu, ... dst
d1.minute, d1.hour, d1.weekday(), d1.day, d1.month, d1.year

Out[12]:

(33, 13, 2, 28, 4, 2021)

In [118]:

hari_ = {0:"Senin", 1:"Selasa", 2:"Rabu", 3:"Kamis", 4:"Jumat", 5:"Sabtu", 6:"Minggu"}
hari_[d1.weekday()]

Out[118]:

'Rabu'

In [119]:

# Cara lain (Menggunakan module tambahan)
import calendar

calendar.day_name[d1.weekday()]

Out[119]:

'Wednesday'

Hari/Saat ini?¶

In [121]:

datetime.today().strftime('%Y-%m-%d ~ %H:%M:%S')

Out[121]:

'2021-04-28 ~ 10:14:51'

Operasi pada variabel datetime¶

In [20]:

# Mana yang lebih dahulu
d1 = datetime.strptime('Apr 28 2021  1:33PM', '%b %d %Y %I:%M%p')
d2 = datetime.strptime('Apr 27 2021  1:33PM', '%b %d %Y %I:%M%p')

d1<d2, d1>d2, d1==d2, d1<=d2

Out[20]:

(False, True, False, False)

In [28]:

# Menjumlahkan waktu: Contoh kasus Google Map/Ojol
import datetime as dm # ==> hati-hati disini!!!...

d3 = d1 + dm.timedelta( days=1, seconds=10, microseconds=88, milliseconds=100, minutes=33, hours=8, weeks=9 ) # days, seconds, then other fields.
print(d1)
print(d3)

2021-04-28 13:33:00
2021-07-01 22:06:10.100088

Konsekuensi dari DateTime ==> Sekarang kita bisa mengurutkan dengan benar¶

In [30]:

Waktu = [d3, d2, d1]
print(Waktu)
Waktu.sort() # Hati-hati ini "inplace"
print(Waktu)

[datetime.datetime(2021, 7, 1, 22, 6, 10, 100088), datetime.datetime(2021, 4, 27, 13, 33), datetime.datetime(2021, 4, 28, 13, 33)]
[datetime.datetime(2021, 4, 27, 13, 33), datetime.datetime(2021, 4, 28, 13, 33), datetime.datetime(2021, 7, 1, 22, 6, 10, 100088)]

Datetime di Pandas¶

References:¶

• https://towardsdatascience.com/a-complete-guide-to-time-series-data-visualization-in-python-da0ddd2cfb01
• https://machinelearningmastery.com/time-series-data-visualization-with-python/
• https://datascienceanywhere.medium.com/visualizing-time-series-data-in-python-e49fa5d10ea
• Dataset: https://github.com/rashida048/Datasets/blob/master/stock_data.csv

In [42]:

import pandas as pd

fileData = 'data/stock_data.csv'
df = pd.read_csv(fileData, error_bad_lines=False, low_memory = False)
print(df.shape)
df.head()

(3019, 7)

Out[42]:

	Date	Open	High	Low	Close	Volume	Name
0	1/3/2006	39.69	41.22	38.79	40.91	24232729	AABA
1	1/4/2006	41.22	41.90	40.77	40.97	20553479	AABA
2	1/5/2006	40.93	41.73	40.85	41.53	12829610	AABA
3	1/6/2006	42.88	43.57	42.80	43.21	29422828	AABA
4	1/9/2006	43.10	43.66	42.82	43.42	16268338	AABA

In [46]:

# Penting untuk cek Tipe Data Dataframe
# Perhatikan disini tipe data "Date" masih berupa string!!!....
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3019 entries, 0 to 3018
Data columns (total 8 columns):
 #   Column    Non-Null Count  Dtype  
---  ------    --------------  -----  
 0   Date      3019 non-null   object 
 1   Open      3019 non-null   float64
 2   High      3019 non-null   float64
 3   Low       3019 non-null   float64
 4   Close     3019 non-null   float64
 5   Volume    3019 non-null   int64  
 6   Name      3019 non-null   object 
 7   weekdays  3019 non-null   object 
dtypes: float64(4), int64(1), object(3)
memory usage: 188.8+ KB

In [122]:

# Perhatikan sekarang "Date" tidak lagi berupa variable, tapi merupakan index bagi dataframenya
df = pd.read_csv(fileData, parse_dates=True, index_col = "Date", error_bad_lines=False, low_memory = False)
df.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 3019 entries, 2006-01-03 to 2017-12-29
Data columns (total 6 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Open    3019 non-null   float64
 1   High    3019 non-null   float64
 2   Low     3019 non-null   float64
 3   Close   3019 non-null   float64
 4   Volume  3019 non-null   int64  
 5   Name    3019 non-null   object 
dtypes: float64(4), int64(1), object(1)
memory usage: 165.1+ KB

In [123]:

df.head()

Out[123]:

	Open	High	Low	Close	Volume	Name
Date
2006-01-03	39.69	41.22	38.79	40.91	24232729	AABA
2006-01-04	41.22	41.90	40.77	40.97	20553479	AABA
2006-01-05	40.93	41.73	40.85	41.53	12829610	AABA
2006-01-06	42.88	43.57	42.80	43.21	29422828	AABA
2006-01-09	43.10	43.66	42.82	43.42	16268338	AABA

In [95]:

# Karena kita gunakan waktu sebagai index, maka kita bisa mudah dalam melakukan "slice" ke data

df.loc['2009'].head()

Out[95]:

	Open	High	Low	Close	Volume	Name	weekdays	Month
Date
2009-01-02	12.17	12.85	12.12	12.85	10213787	AABA	Jumat	1
2009-01-05	12.72	13.01	12.39	12.86	12029058	AABA	Senin	1
2009-01-06	12.96	13.24	12.88	13.00	10173926	AABA	Selasa	1
2009-01-07	12.71	13.16	12.45	12.71	25302285	AABA	Rabu	1
2009-01-08	12.37	13.07	12.31	13.07	14348008	AABA	Kamis	1

In [51]:

# Basic Line Plot
p = df['Volume'].plot(figsize=(16,8))
# Perhatikan "makna" df['volume']

SeaBorn¶

In [53]:

# Warning Slower .... 
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style="darkgrid")

plt.figure(figsize=(14, 8))
p = sns.lineplot(x='Date', y='Volume', data=df)
plt.show()

Hue : Let's add days¶

In [124]:

# Bisa menggunakan Fungsi "Map": silahkan dicoba sebagai latihan

df['weekdays'] = ['']*df.shape[0]
for i,d in df.iterrows():
    df.loc[i,'weekdays'] =  hari_[i.weekday()] # Perhatikan disini menggunakan i dan bukan d.Date karena waktu=index
    
df.head()

Out[124]:

	Open	High	Low	Close	Volume	Name	weekdays
Date
2006-01-03	39.69	41.22	38.79	40.91	24232729	AABA	Selasa
2006-01-04	41.22	41.90	40.77	40.97	20553479	AABA	Rabu
2006-01-05	40.93	41.73	40.85	41.53	12829610	AABA	Kamis
2006-01-06	42.88	43.57	42.80	43.21	29422828	AABA	Jumat
2006-01-09	43.10	43.66	42.82	43.42	16268338	AABA	Senin

In [125]:

d

Out[125]:

Open          69.79
High          70.13
Low           69.43
Close         69.85
Volume      6613070
Name           AABA
weekdays           
Name: 2017-12-29 00:00:00, dtype: object

In [62]:

plt.figure(figsize=(15,6))
sns.lineplot(x='Date', y='Volume', data=df, hue='weekdays', palette='Set1')
plt.show()
# We have our first insight!!!...

SubPlot¶

In [127]:

p = df.plot(subplots=True, figsize=(10,12))

Seasonality¶

• Resampling berdasarkan bulan (month)
• Filter tahun >2016

In [128]:

# Reduce
df_month = df.resample("M").mean() # dirata-ratakan perbulan. hati-hati hanya bisa jika "waktu" adalah index
df_month.head()
# Perhatikan Date mulai 2006

Out[128]:

	Open	High	Low	Close	Volume
Date
2006-01-31	38.245500	38.694000	37.641500	38.113000	3.400594e+07
2006-02-28	33.141579	33.436842	32.627368	32.975789	2.329848e+07
2006-03-31	31.333478	31.696957	30.929130	31.218696	2.095522e+07
2006-04-30	32.383684	32.790000	31.914737	32.283158	2.200768e+07
2006-05-31	31.744545	32.175455	31.171364	31.517273	2.218047e+07

In [129]:

import matplotlib.dates as mdates # Need this additional function

fig, ax = plt.subplots(figsize=(10, 6))

ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m')) # Supaya label lebih jelas
ax.bar(df_month['2016':].index, df_month.loc['2016':, "Volume"], width=25, align='center')

plt.show()

Seaborn & Seasonality¶

• Butuh Kolom baru "Month"

In [74]:

# Latihan: ganti dengan "map" function
df['Month'] = ['']*df.shape[0]
for i,d in df.iterrows():
    df.loc[i,'Month'] =  i.month # Perhatikan disini menggunakan i dan bukan d.Date karena waktu=index
    
df.head()

Out[74]:

	Open	High	Low	Close	Volume	Name	weekdays	Month
Date
2006-01-03	39.69	41.22	38.79	40.91	24232729	AABA	Selasa	1
2006-01-04	41.22	41.90	40.77	40.97	20553479	AABA	Rabu	1
2006-01-05	40.93	41.73	40.85	41.53	12829610	AABA	Kamis	1
2006-01-06	42.88	43.57	42.80	43.21	29422828	AABA	Jumat	1
2006-01-09	43.10	43.66	42.82	43.42	16268338	AABA	Senin	1

In [75]:

#start, end = '2016-01', '2016-12'
fig, axes = plt.subplots(4, 1, figsize=(10, 16), sharex=True)

for name, ax in zip(['Open', 'Close', 'High', 'Low'], axes):
    sns.boxplot(data = df, x='Month', y=name, ax=ax)
    ax.set_ylabel("")
    ax.set_title(name)
    if ax != axes[-1]:
        ax.set_xlabel('')

Line Plot Revisited with resampling¶

In [76]:

p = df_month['Volume'].plot(figsize=(8, 6))

Resampling bisa juga berdasarkan minggu (dan contrasted with daily)¶

• Perhatikan Business understanding dengan baik.

In [79]:

df_week = df.resample("W").mean()
start, end = '2015-01', '2015-08'

fig, ax = plt.subplots(figsize=(16, 8))

ax.plot(df.loc[start:end, 'Volume'], marker='.', linestyle='-', linewidth = 0.5, label='Daily', color='black')
ax.plot(df_week.loc[start:end, 'Volume'], marker='o', markersize=8, linestyle='-', label='Weekly', color='coral')

ax.set_ylabel("Open")
ax.legend()
plt.show()

Rolling?¶

• moving average, also called a rolling or running average is used to analyze the time-series data by calculating averages of different subsets of the complete dataset. Since it involves taking the average of the dataset over time, it is also called a moving mean (MM) or rolling mean.
• https://www.datacamp.com/community/tutorials/moving-averages-in-pandas
• https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.rolling.html
• https://medium.com/@alexander.mueller/rolling-aggregations-on-time-series-data-with-pandas-80dee5893f9

In [130]:

# Contoh sederhana
import numpy as np

df2 = pd.DataFrame({'B': [0, 1, 2, np.nan, 4]})
print(df2)
df2.rolling(2, win_type='triang').sum()

     B
0  0.0
1  1.0
2  2.0
3  NaN
4  4.0

Out[130]:

	B
0	NaN
1	0.5
2	1.5
3	NaN
4	NaN

In [131]:

df_7d_rolling = df.rolling(window=7, center=False).mean() # Perhatikan centre = true !!!... 

df_7d_rolling.head(10)

Out[131]:

	Open	High	Low	Close	Volume
Date
2006-01-03	NaN	NaN	NaN	NaN	NaN
2006-01-04	NaN	NaN	NaN	NaN	NaN
2006-01-05	NaN	NaN	NaN	NaN	NaN
2006-01-06	NaN	NaN	NaN	NaN	NaN
2006-01-09	NaN	NaN	NaN	NaN	NaN
2006-01-10	NaN	NaN	NaN	NaN	NaN
2006-01-11	41.852857	42.532857	41.441429	42.127143	2.082691e+07
2006-01-12	42.171429	42.642857	41.722857	42.124286	2.006818e+07
2006-01-13	42.140000	42.525714	41.558571	41.971429	2.155571e+07
2006-01-17	41.877143	42.334286	41.288571	41.768571	2.578433e+07

In [132]:

start, end = '2016-06', '2017-05'
fig, ax = plt.subplots(figsize=(16, 8))

ax.plot(df.loc[start:end, 'Volume'], marker='.', linestyle='-', 
        linewidth=0.5, label='Daily')
ax.plot(df_week.loc[start:end, 'Volume'], marker='o', markersize=5, 
        linestyle='-', label = 'Weekly mean volume')
ax.plot(df_7d_rolling.loc[start:end, 'Volume'], marker='.', linestyle='-', label='7d Rolling Average')

ax.set_ylabel('Stock Volume')
ax.legend()
plt.show()

Memvisualisasikan Perubahan (Rasio dengan 1 hari sebelumnya)¶

• Menggunakan fungsi "Shift": The shift function shifts the data before or after the specified amount of time.
• https://pandas.pydata.org/docs/reference/api/pandas.Series.shift.html
• Fungsi Div (membagi), dalam hal ini dengan shift.
• https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.div.html
• https://www.geeksforgeeks.org/python-pandas-dataframe-shift/

In [133]:

df.head()

Out[133]:

	Open	High	Low	Close	Volume	Name	weekdays
Date
2006-01-03	39.69	41.22	38.79	40.91	24232729	AABA	Selasa
2006-01-04	41.22	41.90	40.77	40.97	20553479	AABA	Rabu
2006-01-05	40.93	41.73	40.85	41.53	12829610	AABA	Kamis
2006-01-06	42.88	43.57	42.80	43.21	29422828	AABA	Jumat
2006-01-09	43.10	43.66	42.82	43.42	16268338	AABA	Senin

In [134]:

df.Close.shift().head()

Out[134]:

Date
2006-01-03      NaN
2006-01-04    40.91
2006-01-05    40.97
2006-01-06    41.53
2006-01-09    43.21
Name: Close, dtype: float64

In [135]:

df['Change'] = df.Close.div(df.Close.shift())
p = df['Change'].plot(figsize=(20, 8), fontsize = 16)

In [136]:

df.head()

Out[136]:

	Open	High	Low	Close	Volume	Name	weekdays	Change
Date
2006-01-03	39.69	41.22	38.79	40.91	24232729	AABA	Selasa	NaN
2006-01-04	41.22	41.90	40.77	40.97	20553479	AABA	Rabu	1.001467
2006-01-05	40.93	41.73	40.85	41.53	12829610	AABA	Kamis	1.013669
2006-01-06	42.88	43.57	42.80	43.21	29422828	AABA	Jumat	1.040453
2006-01-09	43.10	43.66	42.82	43.42	16268338	AABA	Senin	1.004860

In [137]:

# Zoom to a year

p = df.loc['2008']['Change'].plot(figsize=(10, 6))

Percent_Change¶

• Percentage change between the current and a prior element.
• https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.pct_change.html

In [101]:

df_month.loc[:, 'pct_change'] = df.Close.pct_change()*100

fig, ax = plt.subplots(figsize=(16, 8))

df_month['pct_change' ].plot(kind='bar', color='coral', ax=ax)
ax.xaxis.set_major_locator(mdates.WeekdayLocator())
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
plt.xticks(rotation=45)
ax.legend()

plt.show()

Differencing (Sangat penting di Statistika/Data science)¶

• First discrete difference of element.
• https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.diff.html

In [140]:

p = df.High.diff().plot(figsize=(18, 9))

In [104]:

# Zoom
p = df.loc['2013'].High.diff().plot(figsize=(18, 9))

Expanding Window: Akumulasi Data¶

• Bayangkan seperti jumlah total kasus Covid, tapi lebih fleksible karena bisa juga menggunakan rata-rata atau simpangan baku.
• https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.expanding.html

In [106]:

fig, ax = plt.subplots(figsize=(16, 8))

ax = df.High.plot(label='High')
ax = df.High.expanding().mean().plot(label='High expanding mean')
ax = df.High.expanding().std().plot(label='High expanding std')

ax.legend(); plt.show()

In [108]:

fig, ax = plt.subplots(figsize=(16, 8))

ax = df.High.plot(label='High')
ax = df.High.expanding().sum().plot(label='High expanding mean')
ax.legend(); plt.show()

# Hati-hati skala data ... Latihan: what to do?

Heat Map¶

• Jauh lebih mudah untuk mendapatkan insight
• Butuh untuk menyesuaikan "Struktur Data"-nya.
• Butuh tambahan kolom "Year
• https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.pivot_table.html
• https://seaborn.pydata.org/generated/seaborn.heatmap.html

In [112]:

# Latihan: ganti dengan "map" function
df['Year'] = ['']*df.shape[0]
for i,d in df.iterrows():
    df.loc[i,'Year'] =  i.year # Perhatikan disini menggunakan i dan bukan d.Date karena waktu=index
    
df.head()

Out[112]:

	Open	High	Low	Close	Volume	Name	weekdays	Month	Change	Year
Date
2006-01-03	39.69	41.22	38.79	40.91	24232729	AABA	Selasa	1	NaN	2006
2006-01-04	41.22	41.90	40.77	40.97	20553479	AABA	Rabu	1	1.001467	2006
2006-01-05	40.93	41.73	40.85	41.53	12829610	AABA	Kamis	1	1.013669	2006
2006-01-06	42.88	43.57	42.80	43.21	29422828	AABA	Jumat	1	1.040453	2006
2006-01-09	43.10	43.66	42.82	43.42	16268338	AABA	Senin	1	1.004860	2006

In [113]:

all_month_year_df = pd.pivot_table(df, values="Open",
                                   index=["Month"],
                                   columns=["Year"],
                                   fill_value=0,
                                   margins=True)
named_index = [[calendar.month_abbr[i] if isinstance(i, int) else i for i in list(all_month_year_df.index)]] # name months
all_month_year_df = all_month_year_df.set_index(named_index)
all_month_year_df.head()

Out[113]:

Year	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	All
Jan	38.245500	27.990500	21.926667	12.084500	16.572105	16.441000	15.719500	19.866667	39.268571	48.310500	30.250526	42.256000	27.439336
Feb	33.141579	30.297368	28.884000	12.601053	15.252632	16.778421	15.445500	20.623684	37.397895	43.861579	29.107000	44.916316	27.321515
Mar	31.333478	30.549545	28.070000	13.255455	16.256087	16.663043	14.986818	22.673000	37.975238	43.929091	34.064545	46.230870	27.995209
Apr	32.383684	30.021000	27.930000	13.802381	17.462500	16.853500	15.231500	23.988636	35.254762	44.590476	36.873810	47.207895	28.401504
May	31.744545	29.288182	26.731905	14.903000	15.965000	17.020476	15.405000	26.160455	34.852857	43.475500	36.822381	49.645909	28.583543

In [117]:

fig, ax = plt.subplots(figsize=(12, 12))

sns.heatmap(all_month_year_df, cmap='RdYlGn_r', robust=True, fmt='.2f', 
                 annot=True, linewidths=.5, annot_kws={'size':11}, 
                 cbar_kws={'shrink':.8, 'label':'Open'}, ax=ax)                       
    
ax.set_yticklabels(ax.get_yticklabels(), rotation=0, fontsize=10)
ax.set_xticklabels(ax.get_xticklabels(), rotation=0, fontsize=10)
plt.title('Average Opening', fontdict={'fontsize':18},    pad=14);

plt.show()

End of Module¶

---

Referensi

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