requests 와 함께 headers 정보의 user-agent 를 같이 넘겨주게 되면 해결 할 수 있어요.
아래는 구글 영화 정보 사이트의 인기차트 내용에 대해서 보는 것인데요.
헤더 정보를 넣는냐 아니냐에 따라서 정보가 달라지는 것을 볼 수 있습니다.
[헤더 정보를 넣었을 때]
import requests
from bs4 import BeautifulSoup
url = "https://play.google.com/store/movies/top"
headers = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/92.0.4515.107 Safari/537.36 Edg/92.0.902.62'}
res = requests.get(url, headers=headers)
soup = BeautifulSoup(res.text, 'html.parser')
movie_list = soup.select('#fcxH9b > div.WpDbMd > c-wiz > div > c-wiz > div > div > c-wiz > c-wiz > c-wiz > div > div.ZmHEEd > div:nth-child(1) > c-wiz > div > div > div.RZEgze > div > div > div.bQVA0c > div > div > div.b8cIId.ReQCgd.Q9MA7b > a > div')
movie_list
[<div class="WsMG1c nnK0zc" title="A Quiet Place Part II">A Quiet Place Part II</div>]
[헤더 정보를 빼고 호출하였을때]
import requests
from bs4 import BeautifulSoup
url = "https://play.google.com/store/movies/top"
res = requests.get(url)
soup = BeautifulSoup(res.text, 'html.parser')
movie_list = soup.select('#fcxH9b > div.WpDbMd > c-wiz > div > c-wiz > div > div > c-wiz > c-wiz > c-wiz > div > div.ZmHEEd > div:nth-child(1) > c-wiz > div > div > div.RZEgze > div > div > div.bQVA0c > div > div > div.b8cIId.ReQCgd.Q9MA7b > a > div')
movie_list
[<div class="WsMG1c nnK0zc" title="F9: The Fast Saga">F9: The Fast Saga</div>,
<div class="WsMG1c nnK0zc" title="Rick and Morty">Rick and Morty</div>,
<div class="WsMG1c nnK0zc" title="Gotron Jerrysis Rickvangelion">Gotron Jerrysis Rickvangelion</div>]
import schedule
import time
def job():
print("I'm working...")
# Run job every 3 second/minute/hour/day/week,
# Starting 3 second/minute/hour/day/week from now
schedule.every(3).seconds.do(job)
schedule.every(3).minutes.do(job)
schedule.every(3).hours.do(job)
schedule.every(3).days.do(job)
schedule.every(3).weeks.do(job)
# Run job every minute at the 23rd second
schedule.every().minute.at(":23").do(job)
# Run job every hour at the 42rd minute
schedule.every().hour.at(":42").do(job)
# Run jobs every 5th hour, 20 minutes and 30 seconds in.
# If current time is 02:00, first execution is at 06:20:30
schedule.every(5).hours.at("20:30").do(job)
# Run job every day at specific HH:MM and next HH:MM:SS
schedule.every().day.at("10:30").do(job)
schedule.every().day.at("10:30:42").do(job)
# Run job on a specific day of the week
schedule.every().monday.do(job)
schedule.every().wednesday.at("13:15").do(job)
schedule.every().minute.at(":17").do(job)
while True:
schedule.run_pending()
time.sleep(1)
Cancel a job
To remove a job from the scheduler, use theschedule.cancel_job(job)method
# 특수문자 없애기-1
characters = "/'!?|*<>:\\"
str_title = "안녕하세요_!@##$%%^^|/'!?|*<>:\\: Goodmorning"
new_title = ''.join(x for x in str_title if x not in characters)
print(new_title)
#출력
안녕하세요_@##$%%^^ Goodmorning
Therangetype represents an immutable sequence of numbers and is commonly used for looping a specific number of times inforloops.
classrange(stop)classrange(start,stop[,step])
The arguments to the range constructor must be integers (either built-inintor any object that implements the__index__special method). If thestepargument is omitted, it defaults to1. If thestartargument is omitted, it defaults to0. Ifstepis zero,ValueErroris raised.
For a positivestep, the contents of a rangerare determined by the formular[i]=start+step*iwherei>=0andr[i]<stop.
For a negativestep, the contents of the range are still determined by the formular[i]=start+step*i, but the constraints arei>=0andr[i]>stop.
A range object will be empty ifr[0]does not meet the value constraint. Ranges do support negative indices, but these are interpreted as indexing from the end of the sequence determined by the positive indices.
Ranges containing absolute values larger thansys.maxsizeare permitted but some features (such aslen()) may raiseOverflowError.
Ranges implement all of thecommonsequence operations except concatenation and repetition (due to the fact that range objects can only represent sequences that follow a strict pattern and repetition and concatenation will usually violate that pattern).
start
The value of thestartparameter (or0if the parameter was not supplied)
stop
The value of thestopparameter
step
The value of thestepparameter (or1if the parameter was not supplied)
The advantage of therangetype over a regularlistortupleis that arangeobject will always take the same (small) amount of memory, no matter the size of the range it represents (as it only stores thestart,stopandstepvalues, calculating individual items and subranges as needed).
Testing range objects for equality with==and!=compares them as sequences. That is, two range objects are considered equal if they represent the same sequence of values. (Note that two range objects that compare equal might have differentstart,stopandstepattributes, for examplerange(0)==range(2,1,3)orrange(0,3,2)==range(0,4,2).)
Changed in version 3.2:Implement the Sequence ABC. Support slicing and negative indices. Testintobjects for membership in constant time instead of iterating through all items.
Changed in version 3.3:Define ‘==’ and ‘!=’ to compare range objects based on the sequence of values they define (instead of comparing based on object identity).
# 상기 주소로 가서 Candlestick class 의 parameter 을 살펴보면 다양한 종류가 있습니다.
# 이전 살펴보기에서 어떤식으로 간략히 그릴수 있는지 살펴봤었고, x, close, high, open, low 값을 통해 그릴 수 있었습니다.
# 이외에도 다양하게 있는데 하나씩 살펴볼려고 합니다.
* 찾아봐도 모르겠는건 과감히~~^^ pass~~^^;;;;;;;;;;;; 누가 좀 알려주세용~^^
* close– Sets the close values.
# 종가 가격
* closesrc– Sets the source reference on Chart Studio Cloud for close .
# Chart Studio Cloud 에서 어떤 셋팅을 하는 것 같다. 주피터에서는 별다른 작동을 안함.
* customdata– Assigns extra data each datum. This may be useful when listening to hover, click and selection events. Note that, “scatter” traces also appends customdata items in the markers DOM elements
Return the absolute value of a number. The argument may be an integer, a floating point number, or an object implementing__abs__(). If the argument is a complex number, its magnitude is returned.
A random forest is a meta estimator that fits a number of classifying decision trees on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with themax_samplesparameter ifbootstrap=True(default), otherwise the whole dataset is used to build each tree.
Changed in version 0.22:The default value ofn_estimatorschanged from 10 to 100 in 0.22.
criterion{“mse”, “mae”}, default=”mse”
The function to measure the quality of a split. Supported criteria are “mse” for the mean squared error, which is equal to variance reduction as feature selection criterion, and “mae” for the mean absolute error.
New in version 0.18:Mean Absolute Error (MAE) criterion.
max_depthint, default=None
The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.
min_samples_splitint or float, default=2
The minimum number of samples required to split an internal node:
If int, then considermin_samples_splitas the minimum number.
If float, thenmin_samples_splitis a fraction andceil(min_samples_split*n_samples)are the minimum number of samples for each split.
Changed in version 0.18:Added float values for fractions.
min_samples_leafint or float, default=1
The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at leastmin_samples_leaftraining samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.
If int, then considermin_samples_leafas the minimum number.
If float, thenmin_samples_leafis a fraction andceil(min_samples_leaf*n_samples)are the minimum number of samples for each node.
Changed in version 0.18:Added float values for fractions.
min_weight_fraction_leaffloat, default=0.0
The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.
max_features{“auto”, “sqrt”, “log2”}, int or float, default=”auto”
The number of features to consider when looking for the best split:
If int, then considermax_featuresfeatures at each split.
If float, thenmax_featuresis a fraction andint(max_features*n_features)features are considered at each split.
If “auto”, thenmax_features=n_features.
If “sqrt”, thenmax_features=sqrt(n_features).
If “log2”, thenmax_features=log2(n_features).
If None, thenmax_features=n_features.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more thanmax_featuresfeatures.
max_leaf_nodesint, default=None
Grow trees withmax_leaf_nodesin best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.
min_impurity_decreasefloat, default=0.0
A node will be split if this split induces a decrease of the impurity greater than or equal to this value.
The weighted impurity decrease equation is the following:
whereNis the total number of samples,N_tis the number of samples at the current node,N_t_Lis the number of samples in the left child, andN_t_Ris the number of samples in the right child.
N,N_t,N_t_RandN_t_Lall refer to the weighted sum, ifsample_weightis passed.
New in version 0.19.
min_impurity_splitfloat, default=None
Threshold for early stopping in tree growth. A node will split if its impurity is above the threshold, otherwise it is a leaf.
Deprecated since version 0.19:min_impurity_splithas been deprecated in favor ofmin_impurity_decreasein 0.19. The default value ofmin_impurity_splithas changed from 1e-7 to 0 in 0.23 and it will be removed in 0.25. Usemin_impurity_decreaseinstead.
bootstrapbool, default=True
Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.
oob_scorebool, default=False
whether to use out-of-bag samples to estimate the R^2 on unseen data.
Controls both the randomness of the bootstrapping of the samples used when building trees (ifbootstrap=True) and the sampling of the features to consider when looking for the best split at each node (ifmax_features<n_features). SeeGlossaryfor details.
verboseint, default=0
Controls the verbosity when fitting and predicting.
warm_startbool, default=False
When set toTrue, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. Seethe Glossary.
ccp_alphanon-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller thanccp_alphawill be chosen. By default, no pruning is performed. SeeMinimal Cost-Complexity Pruningfor details.
New in version 0.22.
max_samplesint or float, default=None
If bootstrap is True, the number of samples to draw from X to train each base estimator.
If None (default), then drawX.shape[0]samples.
If int, then drawmax_samplessamples.
If float, then drawmax_samples*X.shape[0]samples. Thus,max_samplesshould be in the interval(0,1).
New in version 0.22.
Attributes
base_estimator_DecisionTreeRegressor
The child estimator template used to create the collection of fitted sub-estimators.
y_truearray-like of shape (n_samples,) or (n_samples, n_outputs)
Ground truth (correct) target values.
y_predarray-like of shape (n_samples,) or (n_samples, n_outputs)
Estimated target values.
sample_weightarray-like of shape (n_samples,), optional
Sample weights.
multioutputstring in [‘raw_values’, ‘uniform_average’] or array-like of shape (n_outputs)
Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
‘raw_values’ :
Returns a full set of errors in case of multioutput input.
‘uniform_average’ :
Errors of all outputs are averaged with uniform weight.
Returns
lossfloat or ndarray of floats
If multioutput is ‘raw_values’, then mean absolute error is returned for each output separately. If multioutput is ‘uniform_average’ or an ndarray of weights, then the weighted average of all output errors is returned.
MAE output is non-negative floating point. The best value is 0.0.
// 값이 작을 수록 좋기 때문에 초기 값은 매우 큰 값으로 정의함.
// LightGBM 에서 DataFrame 이 잘 처리 되지 않는 것을 방지하기 위해서 .values 를 사용하였다.
06. Ch 23. 진짜 문제를 해결해 보자 (1) - 상점 신용카드 매출 예측 - 05. (5) 모델 적용
// 모델 학습이 다 끝나서 새로 들어온 데이터에서 대해서 예측을 해보는 것이다.
// pipeline 을 이용해서 구축할 수 있다.
07. Ch 24. 진짜 문제를 해결해 보자 (2) - 아파트 실거래가 예측 - 01. (1) 문제 소개
* 아파트 실거래가 예측
// 실제 데이터는 크기가 크기 때문에 샘플 데이터로 주었다.
// 참조 데이터는 대회 문제 해결을 위해, 강사가 직접 수집한 데이터이며, 어떠한 정제도 하지 않았다.
08. Ch 24. 진짜 문제를 해결해 보자 (2) - 아파트 실거래가 예측 - 02. (2) 변수 변환 및 부착
Calculateselement in test_elements, broadcasting overelementonly. Returns a boolean array of the same shape aselementthat is True where an element ofelementis intest_elementsand False otherwise.
Parameters
elementarray_like
Input array.
test_elementsarray_like
The values against which to test each value ofelement. This argument is flattened if it is an array or array_like. See notes for behavior with non-array-like parameters.
assume_uniquebool, optional
If True, the input arrays are both assumed to be unique, which can speed up the calculation. Default is False.
invertbool, optional
If True, the values in the returned array are inverted, as if calculatingelement not in test_elements. Default is False.np.isin(a,b,invert=True)is equivalent to (but faster than)np.invert(np.isin(a,b)).
Returns
isinndarray, bool
Has the same shape aselement. The valueselement[isin]are intest_elements.
Thepicklemodule implements binary protocols for serializing and de-serializing a Python object structure.“Pickling”is the process whereby a Python object hierarchy is converted into a byte stream, and“unpickling”is the inverse operation, whereby a byte stream (from abinary fileorbytes-like object) is converted back into an object hierarchy. Pickling (and unpickling) is alternatively known as “serialization”, “marshalling,”1or “flattening”; however, to avoid confusion, the terms used here are “pickling” and “unpickling”.
JSON is a text serialization format (it outputs unicode text, although most of the time it is then encoded toutf-8), while pickle is a binary serialization format;
JSON is human-readable, while pickle is not;
JSON is interoperable and widely used outside of the Python ecosystem, while pickle is Python-specific;
JSON, by default, can only represent a subset of the Python built-in types, and no custom classes; pickle can represent an extremely large number of Python types (many of them automatically, by clever usage of Python’s introspection facilities; complex cases can be tackled by implementingspecific object APIs);
Unlike pickle, deserializing untrusted JSON does not in itself create an arbitrary code execution vulnerability.
sampling_strategyfloat, str, dict or callable, default=’auto’
Sampling information to resample the data set.
Whenfloat, it corresponds to the desired ratio of the number of samples in the minority class over the number of samples in the majority class after resampling. Therefore, the ratio is expressed as
where
is the number of samples in the minority class after resampling and
is the number of samples in the majority class.
Warning
floatis only available forbinaryclassification. An error is raised for multi-class classification.
Whenstr, specify the class targeted by the resampling. The number of samples in the different classes will be equalized. Possible choices are:
'minority': resample only the minority class;
'notminority': resample all classes but the minority class;
'notmajority': resample all classes but the majority class;
'all': resample all classes;
'auto': equivalent to'notmajority'.
Whendict, the keys correspond to the targeted classes. The values correspond to the desired number of samples for each targeted class.
When callable, function takingyand returns adict. The keys correspond to the targeted classes. The values correspond to the desired number of samples for each class.
If int,random_stateis the seed used by the random number generator;
IfRandomStateinstance, random_state is the random number generator;
IfNone, the random number generator is theRandomStateinstance used bynp.random.
k_neighborsint or object, default=5
If int, number of nearest neighbours to used to construct synthetic samples. If object, an estimator that inherits from sklearn.neighbors.base.KNeighborsMixin that will be used to find the k_neighbors.
n_jobsint, default=None
Number of CPU cores used during the cross-validation loop. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
Whenfloat, it corresponds to the desired ratio of the number of samples in the minority class over the number of samples in the majority class after resampling. Therefore, the ratio is expressed as
where
is the number of samples in the minority class and
is the number of samples in the majority class after resampling.
Warning
floatis only available forbinaryclassification. An error is raised for multi-class classification.
Whenstr, specify the class targeted by the resampling. The number of samples in the different classes will be equalized. Possible choices are:
'majority': resample only the majority class;
'notminority': resample all classes but the minority class;
'notmajority': resample all classes but the majority class;
'all': resample all classes;
'auto': equivalent to'notminority'.
Whendict, the keys correspond to the targeted classes. The values correspond to the desired number of samples for each targeted class.
When callable, function takingyand returns adict. The keys correspond to the targeted classes. The values correspond to the desired number of samples for each class.
versionint, default=1
Version of the NearMiss to use. Possible values are 1, 2 or 3.
n_neighborsint or object, default=3
If int, size of the neighbourhood to consider to compute the average distance to the minority point samples. If object, an estimator that inherits from sklearn.neighbors.base.KNeighborsMixin that will be used to find the k_neighbors.
n_neighbors_ver3int or object, default=3
If int, NearMiss-3 algorithm start by a phase of re-sampling. This parameter correspond to the number of neighbours selected create the subset in which the selection will be performed. If object, an estimator that inherits from sklearn.neighbors.base.KNeighborsMixin that will be used to find the k_neighbors.
n_jobsint, default=None
Number of CPU cores used during the cross-validation loop. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
[05. Part 5) Ch 20. 편향된 모델은 쓸모 없어 - 클래스 불균형 문제 - 02-2. 재샘플링 - 오버샘플링과 언더 샘플링(실습)]
// kNN 을 사용해서 클래스 불균형도 테스트를 해준다.
// KNeighborsClassifier
// 재현율 0% 로 불균형이 심각한 수준이라 볼 수 있다.
[05. Part 5) Ch 20. 편향된 모델은 쓸모 없어 - 클래스 불균형 문제 - 03-1 비용 민감 모델 (이론)]
// 모델의 학습 변경한 모델이라고 볼 수 있다. 전처리라고 보기는 좀 어렵다.
* 정의
// 비용을 위양성 비용보다 크게 설정
* 확률 모델
* 관련문법: .predict_proba
* Tip.Numpy 와 Pandas 잘 쓰는 기본 원칙 : 가능하면 배열 단위 연산을 하라
// 유니버설 함수, 브로드캐스팅, 마스크 연산을 최대한 활용
* 비확률 모델 (1) 서포트 벡터 머신
* 비확률 모델 (2) 의사결정 나무
* 관련문법 : class_weight
[05. Part 5) Ch 20. 편향된 모델은 쓸모 없어 - 클래스 불균형 문제 - 03-2 비용 민감 모델 (실습)]
The multiclass support is handled according to a one-vs-one scheme.
For details on the precise mathematical formulation of the provided kernel functions and how gamma, coef0 and degree affect each other, see the corresponding section in the narrative documentation: Kernel functions.
Regularization parameter. The strength of the regularization is inversely proportional to C. Must be strictly positive. The penalty is a squared l2 penalty.
Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. If a callable is given it is used to pre-compute the kernel matrix from data matrices; that matrix should be an array of shape (n_samples, n_samples).
degreeint, default=3
Degree of the polynomial kernel function (‘poly’). Ignored by all other kernels.
gamma{‘scale’, ‘auto’} or float, default=’scale’
Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.
ifgamma='scale'(default) is passed then it uses 1 / (n_features * X.var()) as value of gamma,
if ‘auto’, uses 1 / n_features.
Changed in version 0.22: The default value of gamma changed from ‘auto’ to ‘scale’.
coef0float, default=0.0
Independent term in kernel function. It is only significant in ‘poly’ and ‘sigmoid’.
shrinkingbool, default=True
Whether to use the shrinking heuristic. See the User Guide.
probabilitybool, default=False
Whether to enable probability estimates. This must be enabled prior to calling fit, will slow down that method as it internally uses 5-fold cross-validation, and predict_proba may be inconsistent with predict. Read more in the User Guide.
tolfloat, default=1e-3
Tolerance for stopping criterion.
cache_sizefloat, default=200
Specify the size of the kernel cache (in MB).
class_weightdict or ‘balanced’, default=None
Set the parameter C of class i to class_weight[i]*C for SVC. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))
verbosebool, default=False
Enable verbose output. Note that this setting takes advantage of a per-process runtime setting in libsvm that, if enabled, may not work properly in a multithreaded context.
max_iterint, default=-1
Hard limit on iterations within solver, or -1 for no limit.
Whether to return a one-vs-rest (‘ovr’) decision function of shape (n_samples, n_classes) as all other classifiers, or the original one-vs-one (‘ovo’) decision function of libsvm which has shape (n_samples, n_classes * (n_classes - 1) / 2). However, one-vs-one (‘ovo’) is always used as multi-class strategy. The parameter is ignored for binary classification.
Changed in version 0.19: decision_function_shape is ‘ovr’ by default.
New in version 0.17: decision_function_shape=’ovr’ is recommended.
Changed in version 0.17: Deprecated decision_function_shape=’ovo’ and None.
break_tiesbool, default=False
If true, decision_function_shape='ovr', and number of classes > 2, predict will break ties according to the confidence values of decision_function; otherwise the first class among the tied classes is returned. Please note that breaking ties comes at a relatively high computational cost compared to a simple predict.
New in version 0.22.
random_stateint or RandomState instance, default=None
Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See Glossary.
Attributes
support_ndarray of shape (n_SV,)
Indices of support vectors.
support_vectors_ndarray of shape (n_SV, n_features)
Support vectors.
n_support_ndarray of shape (n_class,), dtype=int32
Number of support vectors for each class.
dual_coef_ndarray of shape (n_class-1, n_SV)
Dual coefficients of the support vector in the decision function (see Mathematical formulation), multiplied by their targets. For multiclass, coefficient for all 1-vs-1 classifiers. The layout of the coefficients in the multiclass case is somewhat non-trivial. See the multi-class section of the User Guide for details.
coef_ndarray of shape (n_class * (n_class-1) / 2, n_features)
Weights assigned to the features (coefficients in the primal problem). This is only available in the case of a linear kernel.
coef_ is a readonly property derived from dual_coef_ and support_vectors_.
intercept_ndarray of shape (n_class * (n_class-1) / 2,)
Constants in decision function.
fit_status_int
0 if correctly fitted, 1 otherwise (will raise warning)
classes_ndarray of shape (n_classes,)
The classes labels.
probA_ndarray of shape (n_class * (n_class-1) / 2)probB_ndarray of shape (n_class * (n_class-1) / 2)
If probability=True, it corresponds to the parameters learned in Platt scaling to produce probability estimates from decision values. If probability=False, it’s an empty array. Platt scaling uses the logistic function 1 / (1 + exp(decision_value * probA_ + probB_)) where probA_ and probB_ are learned from the dataset [2]. For more information on the multiclass case and training procedure see section 8 of [1].
class_weight_ndarray of shape (n_class,)
Multipliers of parameter C for each class. Computed based on the class_weight parameter.
shape_fit_tuple of int of shape (n_dimensions_of_X,)
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