Complete personal notes for performing Data Analysis, Preprocessing, and Training ML model. For easy guideline and quick copy paste snippet to real work. Fit on one page and constantly updated.
- Preparation
- Importer
- Input Output
- From Other Source : Flat File, SQL, AWS Athena, GSpread
- Scraping : BeautifulSoup, Scrapy
- Exploratory Data Analysis
- Preprocessing
- Training Model
- Neural Network
- Tensor Flow :
- Keras Model :
- PyTorch :
- Miscellaneous
# Most used
import numpy as np # numerical analysis and matrix computation
import pandas as pd # data manipulation and analysis on tabular data
import matplotlib.pyplot as plt # plotting data
import seaborn as sns # data visualization based on matplotlib
# Connection to data
import pymysql # connect to mysql database
import pyodbc # connect to sql server database
import pyathena # connect to aws athena
import gspread # connect to gspread
from oauth2client.service_account import ServiceAccountCredentials # google auth
from gspread_dataframe import get_as_dataframe, set_with_dataframe # library i/o directly from df
# Scikit-learn
from sklearn.preprocessing import Imputer, scale, StandardScaler
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, RandomizedSearchCV
from sklearn.metrics import mean_squared_error, classification_report, confusion_matrix, roc_curve, roc_auc_score
from sklearn.pipeline import Pipeline
# Scikit-learn Model
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LinearRegression, Lasso, Ridge, ElasticNet, LogisticRegression
from sklearn.svm import SVC
# Other Tools
%reload_ext dotenv # reload dotenv on jupyter notebook
%dotenv # load dotenv
import os # os interface, directory, path
import glob # find file on directory with wildcard
import pickle # save/load object on python into/from binary file
import re # find regex pattern on string
import scipy # scientific computing
import statsmodels.api as sm # statistic lib for python
import requests # http for human
from bs4 import BeautifulSoup # tool for scrape web pageCreate DataFrame from list / dict.
data = [{'name': 'A', 'height': 172, 'weight': 78},
{'name': 'B', 'height': 168, 'weight': 75},
{'name': 'C', 'height': 183, 'weight': 81},
{'name': 'D', 'height': 175, 'weight': 77}]
pd.DataFrame(data) # from list of dict
data = {'name': ['A', 'B', 'C', 'D'],
'height': [172, 168, 183, 175],
'weight': [78, 75, 81, 77]}
pd.DataFrame(data) # from dict
data = [('A', 172, 78),
('B', 168, 75),
('C', 183, 81),
('D', 175, 77)]
columns = ['name', 'height', 'weight']
pd.DataFrame(data, columns=columns) # from recordsGenerate random data.
X = np.random.randn(100, 3) # 100 x 3 random std normal dist array
X = np.random.normal(1, 2, size=(100, 3)) # 100 x 3 random normal with mean 1 and stddev 2
from sklearn.datasets import make_regression, make_classification, make_blobs
# generate 100 row data for regression with 10 feature but only 5 informative
X, y = make_regression(n_samples=100, n_features=10, n_informative=5, noise=0.0, random_state=42)
# generate 100 row data for classification with 10 feature but only 5 informative with 3 classes
X, y = make_classification(n_samples=100, n_features=10, n_informative=5, n_classes=3, random_state=42)
# generate 100 row data for clustering with 10 feature with 3 cluster
X, y = make_blobs(n_samples=100, n_features=10, centers=3, cluster_std=1.0, random_state=42)Load sample data.
from sklearn.datasets import load_boston, load_digits, load_iris
d = load_boston() # load data dict 'like' of numpy.ndarray
df = pd.DataFrame(d.data, columns=d.feature_names) # create dataframe with column name
df['TargetCol'] = d.target # add TargetCol columndf = pd.read_csv('data.csv', sep=',', index_col='col1', na_values='-', parse_dates=True)
df = pd.read_excel('data.xlsx', sheet_name='Sheet1', usecols='A,C,E:F')con = pymysql.connect(user=user, password=pwd, database=db, host=host) # mysql
con = pyodbc.connect('DRIVER={ODBC Driver 11 for SQL Server};
SERVER=server_name;DATABASE=db_name;UID=username;PWD=password') # sql server
con = create_engine('mysql+pymysql://username:password@host/database') # with sqlalchemy.create_engine
query = 'select * from employee where name = %(name)s'
df = pd.read_sql(query, con, params={'name': 'value'}) # qieru or table name
df.to_sql(table_name, con, index=False, if_exists='replace') # if_exists = replace/appendconn = pyathena.connect(aws_access_key_id=id, aws_secret_access_key=secret,
s3_staging_dir=stgdir, region_name=region)
query = 'select * from employee'
df = pd.read_sql(query, conn)scope = ['https://spreadsheets.google.com/feeds','https://www.googleapis.com/auth/drive']
creds = ServiceAccountCredentials.from_json_keyfile_name('client_secret.json', scope)
client = gspread.authorize(creds)
sheet = client.open('FileNameOnGDrive').get_worksheet(0)
df = get_as_dataframe(sheet, usecols=list(range(10))) # use additional gspread_dataframe lib
data = sheet.get_all_values()
header = data.pop(0)
df = pd.DataFrame(data, columns=header) # only use gspreadHEADERS = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/76.0.3809.132 Safari/537.36'}
res = requests.get(url, headers=HEADERS) # request url, with user agent on headers
soup = bs4.BeautifulSoup(res.content, 'html.parser') # create soup object
rows = soup.select('div.product') # selector, see misc
text = soup.select('div.product > a').text # get text of link
href = soup.select('div.product > a')['href'] # get href attribute of link# Shell command:
scrapy startproject project_name # create new project
cd project_name
scrapy genspider spider_name url # generate new spider
scrapy crawl spider_name # run spider
scrapy crawl spider_name -o result.csv # run spider, save output as csv
scrapy shell url # testing shell to specific url
# Spider example 1 :
class QuotesSpider(scrapy.Spider):
name = 'quotes'
start_urls = ['http://quotes.toscrape.com']
def parse(self, response):
self.log('I just visited: ' + response.url)
# List of quotes
for quote in response.css('div.quote'):
item = {'author_name': quote.css('small.author::text').extract_first(),
'text': quote.css('span.text::text').extract_first(),
'tags': quote.css('a.tag::text').extract()}
yield item
# Follow pagination link
next_link = response.css('li.next > a::attr(href)').extract_first()
if next_link:
full_next_link = response.urljoin(rel_link)
yield scrapy.Request(url=full_next_link, callback=self.parse)df.col1 # return series col1, easy way
df['col1'] # return series col1, robust way
df[['col1', 'col2']] # return dataframe consist col1 and col2
df.loc[5:10, ['col1','col2']] # return dataframe from row 5:10 column col1 and col2
df.iloc[5:10, 3:5] # return dataframe from row 5:10 column 3:5
df.head() # return first 5 rows, df.tail() return last 5 rows
df[df.col1 == 'abc'] # filter by comparison, use ==, !=, >, <, >=, <=
df[(df.col1 == 'abc') & (df.col2 > 50)] # conditional filter, use &(and), |(or), ~(not), ^(xor), .any(), .all()
df[df.col1.isna()] # filter by col1 is na
df[df.col1.isnull()] # filter by col1 is null, otherwise use .notnull()
df[df.col1.isin(['a','b'])] # filter by col1 is in list
df[df.col1.between(70, 80)] # filter by col1 value between 2 values
df.filter(regex = 'pattern') # filter by regex pattern, see misc
for idx, row in df.iterrows(): # iterate dataframe by rows
print(row['col1']) # return index and series of row
pd.options.display.max_rows = len(df) # default 60 rowsdf.shape # number of rows and cols
df.columns # columns dataframe
df.index # index dataframe
df.T # transpose dataframe
df.info() # info number of rows and cols, dtype each col, memory size
df.describe(include='all') # statistical descriptive: unique, mean, std, min, max, quartile
df.skew() # degree of symetrical, 0 symmetry, + righthand longer, - lefthand longer
df.kurt() # degree of peakedness, 0 normal dist, + too peaked, - almost flat
df.corr() # correlation matrix
df.isnull().sum() # count null value each column, df.isnull() = df.isna()
df.col1.unique() # return unique value of col1
df.nunique() # unique value each column
df.sample(10) # return random sample 10 rows
df['col1'].value_counts(normalize=True) # frequency each value
df.sort_values(['col1'], ascending=True) # sort by col1 ascending, .sort_index() for index
df.drop_duplicates(subset='col1', keep='first', inplace=True) # drop duplicate based on subsetdf.sum() # use sum, count, median, min, mean, var, std, nunique, quantile([0.25,0.75])
df.groupby(['col1']).size() # group by col1
df.groupby(df.col1).TargetCol.agg([np.mean, 'count']) # multi aggregate function on 1 column
df.groupby('col1').agg({'col2': 'count', 'col3': 'mean'}) # multi aggregate function on multi columns
df.pivot(index='col1', columns='col2', values='col3') # reshape to pivot, error when duplicate
df.pivot_table(index='col1', columns='col2', values='col3', aggfunc='sum') # pivot table, like excel
flat = pd.DataFrame(df.to_records()) # flatten multiindex dataframeplt.scatter(x, y, c, s)
# x, y, c, s array like object, c (color) can be color format string, s (size) can be scalar
# also df.plot.scatter(x='col1', y='col2', c='col3', s='col4') or
# sns.scatterplot(x='col1', y='col2', hue='col3', size='col4', style='col5', data=df)
plt.plot(x, y, 'ro--')
# x and y array like object, 'ro--' means red circle marker with dash line (see matplotlib cheatsheet below)
# also written as plt.plot(x, y, color='r', marker='o', linestyle='--') you can also use df.plot() or
# sns.lineplot(x='col1', y='col2', hue='col3', size='col4', data=df)
sns.jointplot(x='col1', y='col2', data=df, kind='reg') # kind = scatter/reg/resid/kde/hex
# Joint 2 two type distrbution plot and kind plot
sns.pairplot(df, x_vars=['col1'], y_vars=['col2'], hue='col3', kind='scatter', diag_kind='auto')
# multi joint plot, _vars for filter column, kind = scatter/reg, diag_kind = hist/kde
regplot(x='col1', y='col2', data=df, ci=95, order=1)
# scatter plot + regression fit, ci (confidence interval 0-100), order (polynomial order)
plt.pie(x, labels, explode, autopct='%1.1f%%')
# x, labels, explode array like also df.plot.pie(y='col1') lable get from index 
plt.hist(x, bins=50, density=False, cumulative=False)
# x array like, density (probability density), cumulative probability
# also df.plot.hist('col1') or sns.distplot(x)
plt.bar(x, y) # or plt.barh(x, y)
# x array like, also df.plot.bar(x='col1', y=['col2','col3'], stacked=True, subplots=False)
# sns.countplot(x='col1', y='col2', hue='col3', data=df, orient='v')
# sns.barplot(x='col1', y='col2', hue='col3', data=df, orient='v')
sns.stripplot(x='col1', y='col2', hue='col3', data=df, jitter=True, dodge=False, orient='v')
# for few data, jitter=True makes point not overwrite on top each other
sns.swarmplot(x='col1', y='col2', hue='col3', data=df, dodge=False, orient='v')
# for few data, more clearly than stripplot, dodge=True makes each cat in hue separable
sns.boxplot(x='col1', y='col2', hue='col3', data=df, dodge=False, orient='v')
# for large data, include median, Q1 & Q3, IQR (Q3-Q1), min (Q1-1.5*IQR), max (Q3+1.5*IQR) and outliers
sns.violinplot(x='col1', y='col2', hue='col3', data=df, dodge=False, orient='v')
# kernel density plot (KDE) for visualize clearly distribution of data
sns.catplot(x='col1', y='col2', hue='col3', data=df, row='col4', col='col5', col_wrap=4,
kind='strip', sharex=True, sharey=True, orient='v')
# categorical plot with facetgrid options 
sns.heatmap(df.corr(), annot=True, fmt='.2g', annot_kws={'size': 8}, square=True, cmap=plt.cm.Reds)
# useful for plot correlation, annot (write value data), fmt (format value), square, cmap (color map)
# other option use df.corr().style.background_gradient(cmap='coolwarm').set_precision(2)
plt.figure(figsize=(15,8))
fig, ax = plt.subplots(1, 2, sharex=False, sharey=False, figsize=(15,4)) # subplots, access with ax[0,1]
plt.title('title') # or ax.set_title
plt.xlabel('foo') # or plt.ylabel, ax.set_xlabel, ax.set_ylabel
plt.xticks(x, labels) # x and labels list, or ax.set_xticks
plt.xticks(rotation=90) # rotate xticks
plt.xlim(0, 100) # or ylim, ax.set_xlim, ax.set_ylim
plt.legend(loc='best') # or ax.legend, loc = upper/lower/right/left/center/upper right
plt.rcParams['figure.figsize'] = (16, 10) # setting default figsize
plt.style.use('classic') # find all style on plt.style.available
g = sns.FacetGrid(df, row='col1', col='col2', hue='col3') # comparable subplot row by col1 col by col2
g.map(plt.hist, 'col4', bins=50) # with histogram count col4
g.map(plt.scatter, 'col4', 'col5') # or with scatter plot col4 and col5Basic Operation
df['new_col'] = df.col1 / 1000 # create new column
df = df.drop('col1', axis=1) # drop column
df = df.drop(df[df.col1 == 'abc'].index) # drop row which col1 equal to 'abc'
df.col1 = df.col1.astype(str) # convert column to string, also use 'category', 'int32'
df.col1 = pd.to_numeric(df.col1, error='coerce') # convert column to numeric
df.col1 = pd.to_datetime(df.col1, error='coerce') # convert column to datetime
df.col1 = pd.Categorical(df.col1, categories=['A','B','C'], ordered=True) # convert column to category
df.col1.str[:2] # access string method/properties
df.col1.dt.strftime('%d/%m/%Y') # access datetime method/properties
df.values # convert dataframe to numpy arrayMap, Apply, Applymap
# Map (Series only)
d = {1: 'one', 2: 'two', 3: 'three'}
df.col1.map(d)
# Apply (Series)
df.col1.apply(func) # element wise
# Apply (Dataframe)
sum_col1, sum_col2 = df[['col1', 'col2']].apply(sum) # apply to axis=0 (row)
df['new_col'] = df.apply(lambda x: x[0] + x[1], axis=1) # apply to axis=1 (column)
# Applymap (Dataframe only)
df.applymap(func) # element wise
# Numpy Vectorize
np.vectorize(func)(A) # element wise# Append
df1.append(df2, ignore_index=True) # stacked vertical with reset index
# Concat
pd.concat([df1, df2, df3], ignore_index=True) # stacked vertical with reset index, axis=0
# Join
# Merge
df1.merge(df2, on='key_col')
pd.merge(df1, df2, on='key_col', how='inner') # how: left, right, outer, inner
pd.merge(df1, df2, left_on='lkey_col', right_on='rkey_col')df = df.fillna(0, method=None) # None/backfill/bfill/pad/ffill
imp = Imputer(missing_values='NaN', strategy='mean', axis=0) # strategy = mean/median/most_frequent
imp.fit(X)
X = enc.transform(X) # perform imputing, or use .fit_transformdf = pd.get_dummies(df, columns=['col1'], prefix='col1')
enc = LabelBinarizer() # label with value 0 or 1
enc = LabelEncoder() # label with value 0 to n-1
enc = OrdinalEncoder() # label with value 0 to n-1, multi column
enc = OneHotEncoder(handle_unknown='error') # create dummy n column binarize, handle_unknown = error/ignore
enc.fit(X)
X = enc.transform(X) # perform encoding, or use .fit_transform
X = enc.inverse_transform(X) # decode back to original
scaler = StandardScaler() # scale data to mean 0 and stddev 1
scaler = MinMaxScaler(feature_range=(0, 1)) # scale data to 0 to 1 (can be set as -1 to 1)
scaler = RobustScaler()quantile_range=(25.0, 75.0) # scale data to robust to outlier
scaler = Normalizer(norm='l2') # normalize data
scaler.fit(X)
X = scaler.transform(X) # perform scaling, or use .fit_transform
X = scaler.inverse_transform(X) # scale back to original# Train test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=42, stratify=y)
# Train test split with Numpy
np.random.shuffle(data)
border = round(data.shap[0] * 0.7)
train, test = np.split(data, [border])
# Cross Validation
cv = cross_val_score(model, X, y, cv=5, scoring='r2', ) # (Stratified) KFold CV, return list of score# Classification model
clf = LogisticRegression(penalty='l1', C=1.0)
# C inverse regularization, smaller C stronger reg
clf = KNeighborsClassifier(n_neighbors=3)
# n_neighbors number neighbors
clf = DecisionTreeClassifier(max_depth=3, criterion='entropy')
# criterion = gini/entropy, other params: min_sample_split, min_sample_leaf, max_featuresaccuracy = model.score(X_test, y_test) # get accuracy (depend on model)# Grid Sarch CV: Try every parameter combination
params = {'C': [1, 0.5, 0.1, 0.05, 0.01]}
grid_cv = GridSearchCV(model, params, cv=5, scoring='r2')
grid_cv.fit(X, y) # print grid_cv.best_params_ and grid_cv.best_score_
# Randomized Search CV: Try every parameter combination based on random distribution
params = {'C': scipy.stats.randint(0, 1)}
randomsearch_cv = RandomizedSearchCV(model, params, cv=5, scoirng='r2')
randomsearch_cv.fit(X, y) # print grid_cv.best_params_ and grid_cv.best_score_# Model
model = Sequential()
# First Layer
model.add(Dense(20, activation='relu', input_shape=(X_train.shape[1],)))
# Hidden Layer
model.add(Dense(20, activation='relu'))
# Output Layer
model.add(Dense(CLASS, activation='softmax'))
# Compile Model
model.compile(loss='categorical_crossentropy', optimizer='Adam', metrics=['accuracy'])
# loss = categorical_crossentropy, mean_squared_error
# optimizer = sgd, adam
# Model Summary
model.summary()callback = [ReduceLROnPlateau(patience=5), # reduce learning rate when metrics stop improving
EarlyStopping(patience=5), # stop training when metrics stop improving
ModelCheckpoint(filepath='best_model.h5', save_best_only=True)] # save model every periodhistory = model.fit(X_train, y_train, epochs=100, validation_data=(X_val, y_val), callbacks=callback)
# history.history return dict of every metrics each epochpred = model.predict(X_val).argmax(axis=1) # multiple prediction
pred = model.predict(X_train[0, :].reshape(1, col_shape)).argmax() # single prediction (first sample)
score = round(model.evaluate(X_train, y_train)[1]*100, 2) # metric scoreMath
np.ceil(5.7) # return 6.0
np.floor(5.7) # return 5.0
np.round(5.7) # return 6.0
round(5.7) # return 6
s.min()
s.max()String
s.isalnum() # return True if alphabetic or numeric
s.isnumeric() # return True if numeric
s.isalpha() # return True if alphabetic
'abc' in s # return True if 'abc' found in s, otherwise use not in
s.find('abc') # return index where substring 'abc' found in s
'abcd: {}'.format(x) # replace {} with value of x, use {:,} for thousand separator, {:.2%} for 2 decimal
s.strip() # return removed leading and trailing space, also use lstrip() or rstrip()
'1,2,3'.split(',') # return list of ['1', '2', '3']
','.join(['a', 'b', 'c']) # join string, return 'a,b,c'List, Tuple & Set
s = [1,2,3,4] # or use list(1,2,3,4)
1 in s # return True if 1 found s
s1 + s2 # concate s1 and s2
s[1] # second item in s
s[1:5] # slice s from 1 to 5 (4 element)
s[1:5:-1] # slice s from 1 to 5 backwards
s.index(3) # return index of 3 in s
s.append(3) # append 3 to end of s
s.reverse() # reverse s, not return anything, or use list(reversed(a))
s.sort() # sort s asc, not return anything, or use sorted(s)
s = (1,2,3,4) # create tuple, immutable list, or use tuple(1,2,3,4)
s = set(1,2,3,4) # create set, unique list
list(map(lambda x: x*2, b)) # map list
list(filter(lambda x: x>2, a)) # filter list
for idx, val in enumerate(s): # iterate over list with index
print(idx, val)Dictionary
d = {'a': 1, 'b': 2, 'c': 3} # or use dict(a=1,b=2,c=3)
d.keys() # return all keys, or use list(d)
d.values() # return all values
'a' in d # return True if 'a' is key in d
d['a'] # get value with keys='a'
del d['a'] # remove item with keys='a'
for key, val in d.items(): # iterate over dict
print(key, val)Numpy.Array
a = np.array([[1,2],[3,4]]) # create array 2x2try: # check if there is pickle file
with open('data.pickle', 'rb') as f: # open pickle file
data = pickle.load(f) # load data from pickle
except FileNotFoundError: # if pickle file not found
data = some_process() # run some process to get data
with open('data.pickle', 'wb') as f: # create new blank pickle file
pickle.dump(data, f) # save data to pickleos.listdir() # get all filename on current directory# Cheatsheet:
\d # any number
\D # anything but number
\w # any character
\W # anything but character
. # any character except newline
\b # whitespace
\. # dot character
+ # match 1 or more, use +? for non-greedy
* # match 0 or more, use *? for non-greedy
? # match 0 or 1
^ # start string
$ # end string
() # capturing group, use (?:) for non-capturing group
{} # expect 1-3, ex \d{3} expect 3 digit number, \d{1,3} expect 1-3 digit number
[] # range, ex [A-Z] all capital letter, [abcd] letter a, b, c, d, [^abc] letter NOT a, b, c
| # either or, ex \d{1,4}|[A-z]{4,10} expect 1-4 digit number or 4-10 character word
\n # newline
\s # space
\t # tab
\e # escape
\r # return
# Sample:
'Chapter\s\d{1,4}[\,\.]?\d{0,1}' # get: Chapter 1, Chapter 23, Chapter 649, Chapter 120.5, Chapter 120,5
'[a-zA-Z0-9.]+@[a-zA-Z0-9-]+\.\w+' # get email address
# Usage:
re.findall(r"\w+ly", text) # return ['carefully', 'quickly']
phone = re.sub("[a-zA-Z '()+-]", '', phone) # substitute character with ''
# Cheatsheet:
%Y # Year 4 digit, ex: 1992, 2008, 2014
%y # Year 2 digit, ex: 92, 08, 14
%m # Month 2 digit, ex: 01, 02, ..., 12
%b # Month abbreviation, ex: Jan, Feb, ..., Dec
%B # Month full, ex: January, February, ..., December
%d # Day 2 digit, ex: 01, 02, ..., 31
%w # Weekday 1 digit, ex: 0 (Sunday), 1, ..., 6
%a # Weekday abbreviation, ex: Sun, Mon, ..., Sat
%A # Weekday full, ex: Sunday, Monday, ..., Saturday
%H # Hour (24), ex: 01, 02, ..., 23, 00
%I # Hour (12), ex: 01, 02, ..., 11, 12
%M # Minute, ex: 00, 01, ..., 59
%S # Second, ex: 00, 01, ..., 59
%f # Microsecond, ex: 000000, 000001, ..., 999999
%c # Local datetime representation, ex: Tue Aug 16 21:30:00 1988
%x # Local date representation, ex: 08/16/88
%X # Local time representation, ex: 21:30:00
# Sample:
'%d-%m-%Y %H:%M:%S' # Personal preferred datetime, ex: 16-08-1988 21:30:00
# Usage:
cur_date = datetime.datetime.now() # return current datetime
cur_date.strftime('%d/%m/%Y') # convert datetime object to string
datetime.datetime.strptime('16/08/1988', '%d/%m%Y') # convert string to object
datetime.datetime.fromtimestamp(1575278092) # convert timestamp datetime to datetime# Cheatsheet:
div # div
#abc # id abc
.abc # class abc
div.abc # div with class abc
div.abc.def # div with class both abc and def
div a # a inside div
div > a # a directly inside div
div + p # p immediately after div
div ~ p # p after div
a[target=_blank] # a with attribute target="_blank"
div[style*="border:1px"] # div with style contain "border:1px"
div[style^="border:1px"] # div with style begin with "border:1px"
div[style$="border:1px"] # div with style end with "border:1px"
# Usage:
links = soup.select('div.abc > a')
for link in links:
print(link['href'])# Line Styles Cheatsheet:
- # solid line
-- # dashed line
-. # dash-dot line
: # dotted line
# Marker Styles Cheatsheet:
. # point
o # circle
^ # triangle up, also v, <, >
s # square
p # pentagon
* # star
+ # plus
x # x
| # v line
- # h line
# Color Styles Cheatsheet:
b # blue
g # green
r # red
c # cyan
m # magenta
y # yellow
k # black
w # white
# Cmap Cheatsheet:
viridis, plasma, Reds, cool, hot, coolwarm, hsv, Pastel1, Pastel2, Paired, Set1, Set2, Set3
plt.colormaps() # return all possible cmap
# Usage:
plt.plot(x, y, 'go--')
plt.plot(x, y, color='g', marker='o', linestyle='--')