markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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For Regression taskLoad the dataset. | cal_housing = fetch_california_housing()
print(cal_housing.DESCR)
X = cal_housing.data
y = cal_housing.target
cal_features = cal_housing.feature_names
df = pd.concat((pd.DataFrame(X, columns=cal_features),
pd.DataFrame({'MedianHouseVal': y})), axis=1)
df.head() | _____no_output_____ | Apache-2.0 | 03-tabular/treeinterpreters.ipynb | munnm/XAI-for-practitioners |
Visualizing a Decision TreeYou will need to install the `pydotplus` library. | #!pip install pydotplus
import pydotplus
# Create dataset
X_train, X_test, y_train, y_test = train_test_split(df[cal_features], y, test_size=0.2)
dt_reg = DecisionTreeRegressor(max_depth=3)
dt_reg.fit(X_train, y_train)
dot_data = export_graphviz(dt_reg, out_file="ca_housing.dot",
feature_nam... | _____no_output_____ | Apache-2.0 | 03-tabular/treeinterpreters.ipynb | munnm/XAI-for-practitioners |
Make a sample prediction. | X_test[cal_features].iloc[[0]].transpose()
dt_reg.predict(X_test[cal_features].iloc[[0]]) | _____no_output_____ | Apache-2.0 | 03-tabular/treeinterpreters.ipynb | munnm/XAI-for-practitioners |
The root node is the mean of the labels from the training data. | y_train.mean() | _____no_output_____ | Apache-2.0 | 03-tabular/treeinterpreters.ipynb | munnm/XAI-for-practitioners |
Train a simple Random Forest | rf_reg = RandomForestRegressor()
rf_reg.fit(X_train, y_train)
print(f'Instance 11 prediction: {rf_reg.predict(X_test.iloc[[11]])}')
print(f'Instance 17 prediction: {rf_reg.predict(X_test.iloc[[17]])}')
idx = 11
from treeinterpreter import treeinterpreter
prediction, bias, contributions = treeinterpreter.predict(rf_reg,... | prediction: [4.8203671]
bias + contributions: [4.8203671]
| Apache-2.0 | 03-tabular/treeinterpreters.ipynb | munnm/XAI-for-practitioners |
In fact, we can check that this holds for all elements of the test set: | predictions, biases, contributions = treeinterpreter.predict(
rf_reg, X_test.values)
assert(np.allclose(np.squeeze(predictions), biases + np.sum(contributions, axis=1)))
assert(np.allclose(rf_reg.predict(X_test), biases + np.sum(contributions, axis=1))) | _____no_output_____ | Apache-2.0 | 03-tabular/treeinterpreters.ipynb | munnm/XAI-for-practitioners |
Comparing Contributions across data slices | X1_test = X_test[:X_test.shape[0]//2:]
X2_test = X_test[X_test.shape[0]//2:]
predictions1, biases1, contributions1 = ti.predict(rf_reg, X1_test.values)
predictions2, biases2, contributions2 = ti.predict(rf_reg, X2_test.values)
total_contribs1 = np.mean(contributions1, axis=0)
total_contribs2 = np.mean(contributions2, ... | _____no_output_____ | Apache-2.0 | 03-tabular/treeinterpreters.ipynb | munnm/XAI-for-practitioners |
TreeExplainer with SHAP | from sklearn.model_selection import train_test_split
import xgboost as xgb
import shap
# print the JS visualization code to the notebook
shap.initjs()
import xgboost as xgb
xgb_reg = xgb.XGBClassifier(max_depth=3,
n_estimators=300,
learning_rate=0.05)
xgb_reg.fit... | _____no_output_____ | Apache-2.0 | 03-tabular/treeinterpreters.ipynb | munnm/XAI-for-practitioners |
Bayesian Ridge Regression Part 2 Multiple Features | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
# yahoo finance is used to fetch data
import yfinance as yf
yf.pdr_override()
# input
symbol = 'AMD'
start = '2014-01-01'
end = '2018-08-27'
# Read data
dataset = yf.download(symbol,start,end)
... | Bayesian Ridge Regression Score: 0.9996452147933678
| MIT | Stock_Algorithms/Bayesian_Ridge_Regression_Part2.ipynb | clairvoyant/Deep-Learning-Machine-Learning-Stock |
Application of Linear Algebra in Data Science Here is the Python code to calculate and plot the MSE | import matplotlib.pyplot as plt
x = list(range(1,6)) #data points
y = [1,1,2,2,4] #original values
y_bar = [0.6,1.29,1.99,2.69,3.4] #predicted values
summation = 0
n = len(y)
for i in range(0, n):
# finding the difference between observed and predicted value
difference = y[i] - y_bar[i]
squared_dif... | _____no_output_____ | Apache-2.0 | Linear_Algebra_in_Research.ipynb | adriangalarion/Lab-Activities-1.1 |
Excercises Electric Machinery Fundamentals Chapter 2 Problem 2-15 | %pylab notebook | Populating the interactive namespace from numpy and matplotlib
| Unlicense | Chapman/Ch2-Problem_2-15.ipynb | dietmarw/EK5312 |
Description An autotransformer is used to connect a 12.6-kV distribution line to a 13.8-kV distribution line. It must be capable of handling 2000 kVA. There are three phases, connected Y-Y with their neutrals solidly grounded. | Vl = 12.6e3 # [V]
Vh = 13.8e3 # [V]
Sio = 2000e3 # [VA] | _____no_output_____ | Unlicense | Chapman/Ch2-Problem_2-15.ipynb | dietmarw/EK5312 |
(a) * What must the $N_C / N _{SE}$ turns ratio be to accomplish this connection? (b) * How much apparent power must the windings of each autotransformer handle? (c) * What is the power advantage of this autotransformer system? (d) * If one of the autotransformers were reconnected as an ordinary transformer, what... | a = (Vh/sqrt(3)) / (Vl/sqrt(3))
n_a = 1 / (a-1) # n_a = Nc/Nse
print('''
Nc/Nse = {:.1f}
=============
'''.format(n_a)) |
Nc/Nse = 10.5
=============
| Unlicense | Chapman/Ch2-Problem_2-15.ipynb | dietmarw/EK5312 |
(b)The power advantage of this autotransformer is:$$\frac{S_{IO}}{S_W} = \frac{N_C + N_{SE}}{N_{SE}}$$ | n_b = (10.5 + 1) / 1 # n_b = Sio/Sw
print('Sio/Sw = {:.1f}'.format(n_b)) | Sio/Sw = 11.5
| Unlicense | Chapman/Ch2-Problem_2-15.ipynb | dietmarw/EK5312 |
Since 1/3 of the total power is associated with each phase, **the windings in each autotransformer must handle:** | Sw = Sio / (3*n_b)
print('''
Sw = {:.1f} kVA
==============
'''.format(Sw/1000)) |
Sw = 58.0 kVA
==============
| Unlicense | Chapman/Ch2-Problem_2-15.ipynb | dietmarw/EK5312 |
(c)As determined in (b), the power advantage of this autotransformer system is: | print('''
Sio/Sw = {:.1f}
=============
'''.format(n_b)) |
Sio/Sw = 11.5
=============
| Unlicense | Chapman/Ch2-Problem_2-15.ipynb | dietmarw/EK5312 |
(d)The voltages across each phase of the autotransformer are: | Vh_p = Vh / sqrt(3)
Vl_p = Vl / sqrt(3)
print('''
Vh_p = {:.0f} V
Vl_p = {:.0f} V
'''.format(Vh_p, Vl_p)) |
Vh_p = 7967 V
Vl_p = 7275 V
| Unlicense | Chapman/Ch2-Problem_2-15.ipynb | dietmarw/EK5312 |
The voltage across the common winding ( $N_C$ ) is: | Vnc = Vl_p
print('Vnc = {:.0f} V'.format(Vnc)) | Vnc = 7275 V
| Unlicense | Chapman/Ch2-Problem_2-15.ipynb | dietmarw/EK5312 |
and the voltage across the series winding ( $N_{SE}$ ) is: | Vnse = Vh_p - Vl_p
print('Vnse = {:.0f} V'.format(Vnse)) | Vnse = 693 V
| Unlicense | Chapman/Ch2-Problem_2-15.ipynb | dietmarw/EK5312 |
Therefore, a single phase of the autotransformer connected as an ordinary transformer would be rated at: | print('''
Vnc/Vnse = {:.0f}/{:.0f} Sw = {:.1f} kVA
=================== =============
'''.format(Vnc, Vnse, Sw/1000)) |
Vnc/Vnse = 7275/693 Sw = 58.0 kVA
=================== =============
| Unlicense | Chapman/Ch2-Problem_2-15.ipynb | dietmarw/EK5312 |
Analyze A/B Test ResultsYou may either submit your notebook through the workspace here, or you may work from your local machine and submit through the next page. Either way assure that your code passes the project [RUBRIC](https://review.udacity.com/!/projects/37e27304-ad47-4eb0-a1ab-8c12f60e43d0/rubric). **Please s... | import pandas as pd
import numpy as np
import random
import matplotlib.pyplot as plt
%matplotlib inline
#We are setting the seed to assure you get the same answers on quizzes as we set up
random.seed(42) | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
`1.` Now, read in the `ab_data.csv` data. Store it in `df`. **Use your dataframe to answer the questions in Quiz 1 of the classroom.**a. Read in the dataset and take a look at the top few rows here: | df = pd.read_csv('ab_data.csv')
df.head() | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
b. Use the cell below to find the number of rows in the dataset. | df.shape[0] | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
c. The number of unique users in the dataset. | df.nunique()[0] | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
d. The proportion of users converted. | df['converted'].sum() / df.shape[0] | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
e. The number of times the `new_page` and `treatment` don't match. | df[((df['group'] == 'treatment') & (df['landing_page'] != 'new_page')) | ((df['group'] != 'treatment') & (df['landing_page'] == 'new_page'))].shape[0] | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
f. Do any of the rows have missing values? | df.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 294478 entries, 0 to 294477
Data columns (total 5 columns):
user_id 294478 non-null int64
timestamp 294478 non-null object
group 294478 non-null object
landing_page 294478 non-null object
converted 294478 non-null int64
dtypes: int64(2),... | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
`2.` For the rows where **treatment** does not match with **new_page** or **control** does not match with **old_page**, we cannot be sure if this row truly received the new or old page. Use **Quiz 2** in the classroom to figure out how we should handle these rows. a. Now use the answer to the quiz to create a new dat... | df2 = df[(((df['group'] == 'treatment') & (df['landing_page'] == 'new_page')) | ((df['group'] == 'control') & (df['landing_page'] == 'old_page')))]
df2.head()
# Double Check all of the correct rows were removed - this should be 0
df2[((df2['group'] == 'treatment') == (df2['landing_page'] == 'new_page')) == False].shape... | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
`3.` Use **df2** and the cells below to answer questions for **Quiz3** in the classroom. a. How many unique **user_id**s are in **df2**? | df2.nunique()[0] | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
b. There is one **user_id** repeated in **df2**. What is it? | uid = df2[df2['user_id'].duplicated() == True].index[0]
uid | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
c. What is the row information for the repeat **user_id**? | df2.loc[uid] | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
d. Remove **one** of the rows with a duplicate **user_id**, but keep your dataframe as **df2**. | df2.drop(2893, inplace=True)
df2.shape[0] | /opt/conda/lib/python3.6/site-packages/pandas/core/frame.py:3697: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
errors=errors)
| MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
`4.` Use **df2** in the cells below to answer the quiz questions related to **Quiz 4** in the classroom.a. What is the probability of an individual converting regardless of the page they receive? | df2[df2['converted'] == 1].shape[0] / df2.shape[0] | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
b. Given that an individual was in the `control` group, what is the probability they converted? | df2[(df2['converted'] == 1) & ((df2['group'] == 'control'))].shape[0] / df2[(df2['group'] == 'control')].shape[0] | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
c. Given that an individual was in the `treatment` group, what is the probability they converted? | df2[(df2['converted'] == 1) & ((df2['group'] == 'treatment'))].shape[0] / df2[(df2['group'] == 'treatment')].shape[0] | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
d. What is the probability that an individual received the new page? | df2[df2['landing_page'] == 'new_page'].shape[0] / df2.shape[0] | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
e. Consider your results from parts (a) through (d) above, and explain below whether you think there is sufficient evidence to conclude that the new treatment page leads to more conversions. **The probability of converting for an individual who received the control page is more than that who received the treatment page... | df2.head() | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
a. What is the **conversion rate** for $p_{new}$ under the null? | p_new = df2[(df2['converted'] == 1)].shape[0] / df2.shape[0]
p_new | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
b. What is the **conversion rate** for $p_{old}$ under the null? | p_old = df2[(df2['converted'] == 1)].shape[0] / df2.shape[0]
p_old | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
c. What is $n_{new}$, the number of individuals in the treatment group? | n_new = df2[(df2['landing_page'] == 'new_page') & (df2['group'] == 'treatment')].shape[0]
n_new | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
d. What is $n_{old}$, the number of individuals in the control group? | n_old = df2[(df2['landing_page'] == 'old_page') & (df2['group'] == 'control')].shape[0]
n_old | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
e. Simulate $n_{new}$ transactions with a conversion rate of $p_{new}$ under the null. Store these $n_{new}$ 1's and 0's in **new_page_converted**. | new_page_converted = np.random.choice([1,0],n_new, p=(p_new,1-p_new))
new_page_converted.mean() | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
f. Simulate $n_{old}$ transactions with a conversion rate of $p_{old}$ under the null. Store these $n_{old}$ 1's and 0's in **old_page_converted**. | old_page_converted = np.random.choice([1,0],n_old, p=(p_old,1-p_old))
old_page_converted.mean() | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
g. Find $p_{new}$ - $p_{old}$ for your simulated values from part (e) and (f). | # p_new - p_old
new_page_converted.mean() - old_page_converted.mean() | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
h. Create 10,000 $p_{new}$ - $p_{old}$ values using the same simulation process you used in parts (a) through (g) above. Store all 10,000 values in a NumPy array called **p_diffs**. | p_diffs = []
for _ in range(10000):
new_page_converted = np.random.choice([0, 1], size = n_new, p = [1-p_new, p_new], replace = True).sum()
old_page_converted = np.random.choice([0, 1], size = n_old, p = [1-p_old, p_old], replace = True).sum()
diff = new_page_converted/n_new - old_page_converted/n_old
... | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
i. Plot a histogram of the **p_diffs**. Does this plot look like what you expected? Use the matching problem in the classroom to assure you fully understand what was computed here. | plt.hist(p_diffs);
plt.plot(); | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
j. What proportion of the **p_diffs** are greater than the actual difference observed in **ab_data.csv**? | # (p_diffs > (p_new - p_old))
prop = (p_diffs > df['converted'].sample(10000)).mean()
prop | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
k. Please explain using the vocabulary you've learned in this course what you just computed in part **j.** What is this value called in scientific studies? What does this value mean in terms of whether or not there is a difference between the new and old pages? **Difference is not significant** l. We could also use a... | import statsmodels.api as sm
convert_old = df2[(df2['landing_page'] == 'old_page') & (df2['group'] == 'control')]
convert_new = df2[(df2['landing_page'] == 'new_page') & (df2['group'] == 'treatment')]
n_old = convert_old.shape[0]
n_new = convert_new.shape[0]
n_old, n_new
# df2.head() | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
m. Now use `stats.proportions_ztest` to compute your test statistic and p-value. [Here](http://knowledgetack.com/python/statsmodels/proportions_ztest/) is a helpful link on using the built in. | from statsmodels.stats.proportion import proportions_ztest
(df2['converted'] == 1).sum()
df2.shape[0]
prop
stat, pval = proportions_ztest((df2['converted'] == 1).sum(), df2.shape[0], prop)
stat, pval | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
n. What do the z-score and p-value you computed in the previous question mean for the conversion rates of the old and new pages? Do they agree with the findings in parts **j.** and **k.**? **p val = 0****No** Part III - A regression approach`1.` In this final part, you will see that the result you achieved in the A/B... | df2.head() | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
b. The goal is to use **statsmodels** to fit the regression model you specified in part **a.** to see if there is a significant difference in conversion based on which page a customer receives. However, you first need to create in df2 a column for the intercept, and create a dummy variable column for which page each us... | import statsmodels.api as sm
df2[['control','ab_page']] = pd.get_dummies(df2['group'])
df2.drop(['control','group'],axis=1, inplace=True)
df2.head() | /opt/conda/lib/python3.6/site-packages/pandas/core/frame.py:3140: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-... | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
c. Use **statsmodels** to instantiate your regression model on the two columns you created in part b., then fit the model using the two columns you created in part **b.** to predict whether or not an individual converts. | df2['intercept'] = 1
logit_mod = sm.Logit(df2['converted'], df2[['intercept','ab_page']])
results = logit_mod.fit()
np.exp(-0.0150)
1/np.exp(-0.0150) | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
d. Provide the summary of your model below, and use it as necessary to answer the following questions. | results.summary() | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
e. What is the p-value associated with **ab_page**? Why does it differ from the value you found in **Part II**? **Hint**: What are the null and alternative hypotheses associated with your regression model, and how do they compare to the null and alternative hypotheses in **Part II**? **P value = 0.190** f. Now, you ar... | df_countries = pd.read_csv('countries.csv')
df_countries.head()
df_merged = pd.merge(df2,df_countries, left_on='user_id', right_on='user_id')
df_merged.head()
df_merged[['US','UK','CA']] = pd.get_dummies(df_merged['country'])
df_merged.drop(['country','CA'],axis=1, inplace=True)
df_merged.head()
df_merged['intercept']... | Optimization terminated successfully.
Current function value: 0.366116
Iterations 6
| MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
**US ia having negative coeff which means that conversion rate decreases if person is from US****UK ia having positive coeff which means that conversion rate increases if person is from UK** h. Though you have now looked at the individual factors of country and page on conversion, we would now like to look at an intera... | final_df = df_merged[['user_id','timestamp','landing_page','converted','ab_page','US','UK']]
final_df.head()
final_df['intercept'] = 1
logit_mod = sm.Logit(final_df['ab_page'], final_df[['intercept','US','UK']])
results = logit_mod.fit()
results.summary() | Optimization terminated successfully.
Current function value: 0.760413
Iterations 3
| MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
**'ab_page' column is 1 when an individual receives the treatment and 0 if control.****US ia having positive coeff which means that chance of getting treatment page increases ****UK ia having negative coeff which means that change of getting control page increases** Finishing Up> Congratulations! You have reached the... | from subprocess import call
call(['python', '-m', 'nbconvert', 'Analyze_ab_test_results_notebook.ipynb']) | _____no_output_____ | MIT | Project 3 - Analyze AB Test Results/Analyze_ab_test_results_notebook.ipynb | geochri/Udacity-DAND |
Eurocode 8 - Chapter 3 - seismic_actionraw functions | from streng.codes.eurocodes.ec8.raw.ch3.seismic_action import spectra | _____no_output_____ | MIT | codes/eurocodes/ec8/raw_ch3_seismic_action.ipynb | panagop/streng_jupyters |
spectra αg | print(spectra.αg.__doc__)
αg = spectra.αg(αgR=0.24,
γI=1.20)
print(f'αg = {αg}g') | αg = 0.288g
| MIT | codes/eurocodes/ec8/raw_ch3_seismic_action.ipynb | panagop/streng_jupyters |
S | print(spectra.S.__doc__)
S = spectra.S(ground_type='B',
spectrum_type=1)
print(f'S = {S}') | S = 1.2
| MIT | codes/eurocodes/ec8/raw_ch3_seismic_action.ipynb | panagop/streng_jupyters |
TB | print(spectra.TB.__doc__)
TB = spectra.TB(ground_type='B',
spectrum_type=1)
print(f'TB = {TB}') | TB = 0.15
| MIT | codes/eurocodes/ec8/raw_ch3_seismic_action.ipynb | panagop/streng_jupyters |
TC | print(spectra.TC.__doc__)
TC = spectra.TC(ground_type='B',
spectrum_type=1)
print(f'TC = {TC}') | TC = 0.5
| MIT | codes/eurocodes/ec8/raw_ch3_seismic_action.ipynb | panagop/streng_jupyters |
TD | print(spectra.TD.__doc__)
TD = spectra.TD(ground_type='B',
spectrum_type=1)
print(f'TD = {TD}') | TD = 2.0
| MIT | codes/eurocodes/ec8/raw_ch3_seismic_action.ipynb | panagop/streng_jupyters |
Se | print(spectra.Se.__doc__)
Se = spectra.Se(T=0.50,
αg = 0.24,
S=1.20,
TB=0.15,
TC=0.50,
TD=2.0,
η=1.0)
print(f'Se = {Se}g') | Se = 0.72g
| MIT | codes/eurocodes/ec8/raw_ch3_seismic_action.ipynb | panagop/streng_jupyters |
SDe | print(spectra.SDe.__doc__)
Sde = spectra.SDe(T=0.5,
Se=0.72*9.81)
print(f'Sde = {Sde:.3f}m') | Sde = 0.045m
| MIT | codes/eurocodes/ec8/raw_ch3_seismic_action.ipynb | panagop/streng_jupyters |
dg | print(spectra.dg.__doc__)
dg = spectra.dg(αg=0.24,
S=1.20,
TC=0.50,
TD=2.0)
print(f'dg = {dg:.4f}g') | dg = 0.0072g
| MIT | codes/eurocodes/ec8/raw_ch3_seismic_action.ipynb | panagop/streng_jupyters |
Sd | print(spectra.Sd.__doc__)
Sd = spectra.Sd(T=0.50,
αg = 0.24,
S=1.20,
TB=0.15,
TC=0.50,
TD=2.0,
q=3.9,
β=0.20)
print(f'Sd = {Sd:.3f}g') | Sd = 0.185g
| MIT | codes/eurocodes/ec8/raw_ch3_seismic_action.ipynb | panagop/streng_jupyters |
η | print(spectra.η.__doc__)
η_5 = spectra.η(5)
print(f'η(5%) = {η_5:.2f}')
η_7 = spectra.η(7)
print(f'η(7%) = {η_7:.2f}') | η(5%) = 1.00
η(7%) = 0.91
| MIT | codes/eurocodes/ec8/raw_ch3_seismic_action.ipynb | panagop/streng_jupyters |
Standalone Convergence Checker for the numerical vKdV solverCopied from Standalone Convergence Checker for the numerical KdV solver - just add bathyDoes not save or require any input data | import xarray as xr
from iwaves.kdv.kdvimex import KdVImEx#from_netcdf
from iwaves.kdv.vkdv import vKdV
from iwaves.kdv.solve import solve_kdv
#from iwaves.utils.plot import vKdV_plot
import iwaves.utils.initial_conditions as ics
import numpy as np
from scipy.interpolate import PchipInterpolator as pchip
import matpl... | _____no_output_____ | BSD-2-Clause | sandpit/standalone_vkdv_convergence.ipynb | mrayson/iwaves |
Random Forest Classification Random Forest The fundamental idea behind a random forest is to combine many decision trees into a single model. Individually, predictions made by decision trees (or humans) may not be accurate, but combined together, the predictions will be closer to the mark on average. Pros - can hand... | ### imports ###
import pandas as pd
import numpy as np
import sklearn
df = pd.read_csv('https://raw.githubusercontent.com/CVanchieri/CS_Notes/main/Classification_Notes/bill_authentication.csv') # read in the file
print('data frame shape:', df.shape) # show the data frame shape
df.head() # show the data frame
### ins... | --- bar plots ---
| MIT | Classification_Notes/SKlearn_RandomForest_Classification.ipynb | CVanchieri/CS_Notes |
Encode + Clean + Organize | ### encoding not necessary with this example, all are numericals ###
### check for outliers in the data ###
import matplotlib.pyplot as plt
# view each feature in a boxplot
for column in df:
plt.figure() # plot figure
f, ax = plt.subplots(1, 1, figsize = (10, 7))
df.boxplot([column]) # set data
### funct... | /usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
after removing the cwd ... | MIT | Classification_Notes/SKlearn_RandomForest_Classification.ipynb | CVanchieri/CS_Notes |
Random Forest Classification - GridSearch CV - RandomSearch CV | ### copy the data frame ###
df1 = df.copy()
### split the data into features & target sets ###
X = df1.iloc[:, 0:4].values # set the features
y = df1.iloc[:, 4].values # set the target
print('--- data shapes --- ')
print('X shape:', X.shape)
print('y shape:', y.shape)
### set the train test split parameters ###
fro... | --- distplot accuracy ---
| MIT | Classification_Notes/SKlearn_RandomForest_Classification.ipynb | CVanchieri/CS_Notes |
GridSearch CV | ### copy the data frame ###
df2 = df.copy()
### split the data into features & target sets ###
# for single regression select 1 feature
X = df2.iloc[:, 0:4].values # set the features
y = df2.iloc[:, 4].values # set the target
print('--- data shapes --- ')
print('X shape:', X.shape)
print('y shape:', y.shape)
### set... | --- distplot accuracy ---
| MIT | Classification_Notes/SKlearn_RandomForest_Classification.ipynb | CVanchieri/CS_Notes |
RandomSearch CV | ### copy the data frame ###
df3 = df.copy()
### split the data into features & target sets ###
# for single regression select the 1 feature
X = df3.iloc[:, 0:4].values # set the features
y = df3.iloc[:, 4].values # set the target
print('--- data shapes --- ')
print('X shape:', X.shape) # show the shape
print('y shape... | --- distplot accuracy ---
| MIT | Classification_Notes/SKlearn_RandomForest_Classification.ipynb | CVanchieri/CS_Notes |
.init setup keras-retinanet | !git clone https://github.com/fizyr/keras-retinanet.git
%cd keras-retinanet/
!pip install .
!python setup.py build_ext --inplace | Cloning into 'keras-retinanet'...
remote: Enumerating objects: 4712, done.[K
remote: Total 4712 (delta 0), reused 0 (delta 0), pack-reused 4712[K
Receiving objects: 100% (4712/4712), 14.43 MiB | 36.84 MiB/s, done.
Resolving deltas: 100% (3128/3128), done.
/content/keras-retinanet
Processing /content/keras-retinanet
R... | Apache-2.0 | RetinaNet_Video_Object_Detection.ipynb | thingumajig/colab-experiments |
download model | #!curl -LJO --output snapshots/pretrained.h5 https://github.com/fizyr/keras-retinanet/releases/download/0.5.0/resnet50_coco_best_v2.1.0.h5
import urllib
PRETRAINED_MODEL = './snapshots/_pretrained_model.h5'
URL_MODEL = 'https://github.com/fizyr/keras-retinanet/releases/download/0.5.0/resnet50_coco_best_v2.1.0.h5'
url... | _____no_output_____ | Apache-2.0 | RetinaNet_Video_Object_Detection.ipynb | thingumajig/colab-experiments |
inference modules | !pwd
#import os, sys
#sys.path.insert(0, 'keras-retinanet')
# show images inline
%matplotlib inline
# automatically reload modules when they have changed
%load_ext autoreload
%autoreload 2
import os
#os.environ['CUDA_VISIBLE_DEVICES'] = '0'
# import keras
import keras
from keras_retinanet import models
from ker... | /content/keras-retinanet
| Apache-2.0 | RetinaNet_Video_Object_Detection.ipynb | thingumajig/colab-experiments |
load model | # %cd keras-retinanet/
model_path = os.path.join('snapshots', sorted(os.listdir('snapshots'), reverse=True)[0])
print(model_path)
print(os.path.isfile(model_path))
# load retinanet model
model = models.load_model(model_path, backbone_name='resnet50')
# model = models.convert_model(model)
# load label to names mappin... | snapshots/_pretrained_model.h5
True
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically b... | Apache-2.0 | RetinaNet_Video_Object_Detection.ipynb | thingumajig/colab-experiments |
detect objects |
def img_inference(img_path, threshold_score = 0.8):
image = read_image_bgr(img_path)
# copy to draw on
draw = image.copy()
draw = cv2.cvtColor(draw, cv2.COLOR_BGR2RGB)
# preprocess image for network
image = preprocess_image(image)
image, scale = resize_image(image)
# process image
start = time.ti... | _____no_output_____ | Apache-2.0 | RetinaNet_Video_Object_Detection.ipynb | thingumajig/colab-experiments |
Project: Part of Speech Tagging with Hidden Markov Models --- IntroductionPart of speech tagging is the process of determining the syntactic category of a word from the words in its surrounding context. It is often used to help disambiguate natural language phrases because it can be done quickly with high accuracy. Ta... | # Jupyter "magic methods" -- only need to be run once per kernel restart
%load_ext autoreload
%aimport helpers, tests
%autoreload 1
# import python modules -- this cell needs to be run again if you make changes to any of the files
import matplotlib.pyplot as plt
import numpy as np
from IPython.core.display import HTML... | _____no_output_____ | MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
Step 1: Read and preprocess the dataset---We'll start by reading in a text corpus and splitting it into a training and testing dataset. The data set is a copy of the [Brown corpus](https://en.wikipedia.org/wiki/Brown_Corpus) (originally from the [NLTK](https://www.nltk.org/) library) that has already been pre-processe... | data = Dataset("tags-universal.txt", "brown-universal.txt", train_test_split=0.8)
print("There are {} sentences in the corpus.".format(len(data)))
print("There are {} sentences in the training set.".format(len(data.training_set)))
print("There are {} sentences in the testing set.".format(len(data.testing_set)))
asser... | There are 57340 sentences in the corpus.
There are 45872 sentences in the training set.
There are 11468 sentences in the testing set.
| MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
The Dataset InterfaceWe can access (mostly) immutable references to the dataset through a simple interface provided through the `Dataset` class, which represents an iterable collection of sentences along with easy access to partitions of the data for training & testing. Review the reference below, to make sure you und... | key = 'b100-38532'
print("Sentence: {}".format(key))
print("words:\n\t{!s}".format(data.sentences[key].words))
print("tags:\n\t{!s}".format(data.sentences[key].tags)) | Sentence: b100-38532
words:
('Perhaps', 'it', 'was', 'right', ';', ';')
tags:
('ADV', 'PRON', 'VERB', 'ADJ', '.', '.')
| MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
**Note:** The underlying iterable sequence is **unordered** over the sentences in the corpus; it is not guaranteed to return the sentences in a consistent order between calls. Use `Dataset.stream()`, `Dataset.keys`, `Dataset.X`, or `Dataset.Y` attributes if you need ordered access to the data. Counting Unique ElementsW... | print("There are a total of {} samples of {} unique words in the corpus."
.format(data.N, len(data.vocab)))
print("There are {} samples of {} unique words in the training set."
.format(data.training_set.N, len(data.training_set.vocab)))
print("There are {} samples of {} unique words in the testing set."
... | There are a total of 1161192 samples of 56057 unique words in the corpus.
There are 928458 samples of 50536 unique words in the training set.
There are 232734 samples of 25112 unique words in the testing set.
There are 5521 words in the test set that are missing in the training set.
| MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
Accessing word and tag SequencesThe `Dataset.X` and `Dataset.Y` attributes provide access to ordered collections of matching word and tag sequences for each sentence in the dataset. | # accessing words with Dataset.X and tags with Dataset.Y
for i in range(2):
print("Sentence {}:".format(i + 1), data.X[i])
print()
print("Labels {}:".format(i + 1), data.Y[i])
print() | Sentence 1: ('Mr.', 'Podger', 'had', 'thanked', 'him', 'gravely', ',', 'and', 'now', 'he', 'made', 'use', 'of', 'the', 'advice', '.')
Labels 1: ('NOUN', 'NOUN', 'VERB', 'VERB', 'PRON', 'ADV', '.', 'CONJ', 'ADV', 'PRON', 'VERB', 'NOUN', 'ADP', 'DET', 'NOUN', '.')
Sentence 2: ('But', 'there', 'seemed', 'to', 'be', 'som... | MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
Accessing (word, tag) SamplesThe `Dataset.stream()` method returns an iterator that chains together every pair of (word, tag) entries across all sentences in the entire corpus. | # use Dataset.stream() (word, tag) samples for the entire corpus
print("\nStream (word, tag) pairs:\n")
for i, pair in enumerate(data.stream()):
print("\t", pair)
if i > 5: break |
Stream (word, tag) pairs:
('Mr.', 'NOUN')
('Podger', 'NOUN')
('had', 'VERB')
('thanked', 'VERB')
('him', 'PRON')
('gravely', 'ADV')
(',', '.')
| MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
For both our baseline tagger and the HMM model we'll build, we need to estimate the frequency of tags & words from the frequency counts of observations in the training corpus. The next several cells will complete functions to compute the counts of several sets of counts. Step 2: Build a Most Frequent Class tagger---P... | def pair_counts(sequences_A, sequences_B):
"""Return a dictionary keyed to each unique value in the first sequence list
that counts the number of occurrences of the corresponding value from the
second sequences list.
For example, if sequences_A is tags and sequences_B is the corresponding
words... | _____no_output_____ | MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
IMPLEMENTATION: Most Frequent Class TaggerUse the `pair_counts()` function and the training dataset to find the most frequent class label for each word in the training data, and populate the `mfc_table` below. The table keys should be words, and the values should be the appropriate tag string.The `MFCTagger` class is ... | # Create a lookup table mfc_table where mfc_table[word] contains the tag label most frequently assigned to that word
from collections import namedtuple
FakeState = namedtuple("FakeState", "name")
class MFCTagger:
# NOTE: You should not need to modify this class or any of its methods
missing = FakeState(name="... | _____no_output_____ | MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
Making Predictions with a ModelThe helper functions provided below interface with Pomegranate network models & the mocked MFCTagger to take advantage of the [missing value](http://pomegranate.readthedocs.io/en/latest/nan.html) functionality in Pomegranate through a simple sequence decoding function. Run these function... | def replace_unknown(sequence):
"""Return a copy of the input sequence where each unknown word is replaced
by the literal string value 'nan'. Pomegranate will ignore these values
during computation.
"""
return [w if w in data.training_set.vocab else 'nan' for w in sequence]
def simplify_decoding(X, ... | _____no_output_____ | MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
Example Decoding Sequences with MFC Tagger | for key in data.testing_set.keys[:3]:
print("Sentence Key: {}\n".format(key))
print("Predicted labels:\n-----------------")
print(simplify_decoding(data.sentences[key].words, mfc_model))
print()
print("Actual labels:\n--------------")
print(data.sentences[key].tags)
print("\n") | Sentence Key: b100-28144
Predicted labels:
-----------------
['CONJ', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'CONJ', 'NOUN', 'NUM', '.', '.', 'NOUN', '.', '.']
Actual labels:
--------------
('CONJ', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'CONJ', 'NOUN', 'NUM', '.', '.', 'NOUN... | MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
Evaluating Model AccuracyThe function below will evaluate the accuracy of the MFC tagger on the collection of all sentences from a text corpus. | def accuracy(X, Y, model):
"""Calculate the prediction accuracy by using the model to decode each sequence
in the input X and comparing the prediction with the true labels in Y.
The X should be an array whose first dimension is the number of sentences to test,
and each element of the array should b... | _____no_output_____ | MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
Evaluate the accuracy of the MFC taggerRun the next cell to evaluate the accuracy of the tagger on the training and test corpus. | mfc_training_acc = accuracy(data.training_set.X, data.training_set.Y, mfc_model)
print("training accuracy mfc_model: {:.2f}%".format(100 * mfc_training_acc))
mfc_testing_acc = accuracy(data.testing_set.X, data.testing_set.Y, mfc_model)
print("testing accuracy mfc_model: {:.2f}%".format(100 * mfc_testing_acc))
assert ... | training accuracy mfc_model: 95.72%
testing accuracy mfc_model: 93.00%
| MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
Step 3: Build an HMM tagger---The HMM tagger has one hidden state for each possible tag, and parameterized by two distributions: the emission probabilties giving the conditional probability of observing a given **word** from each hidden state, and the transition probabilities giving the conditional probability of movi... | def unigram_counts(sequences):
"""Return a dictionary keyed to each unique value in the input sequence list that
counts the number of occurrences of the value in the sequences list. The sequences
collection should be a 2-dimensional array.
For example, if the tag NOUN appears 275558 times over all ... | {'ADV': 44877, 'NOUN': 220632, '.': 117757, 'VERB': 146161, 'ADP': 115808, 'ADJ': 66754, 'CONJ': 30537, 'DET': 109671, 'PRT': 23906, 'NUM': 11878, 'PRON': 39383, 'X': 1094}
| MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
IMPLEMENTATION: Bigram CountsComplete the function below to estimate the co-occurrence frequency of each pair of symbols in each of the input sequences. These counts are used in the HMM model to estimate the bigram probability of two tags from the frequency counts according to the formula: $$P(tag_2|tag_1) = \frac{C(t... | def bigram_counts(sequences):
"""Return a dictionary keyed to each unique PAIR of values in the input sequences
list that counts the number of occurrences of pair in the sequences list. The input
should be a 2-dimensional array.
For example, if the pair of tags (NOUN, VERB) appear 61582 times, then... | _____no_output_____ | MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
IMPLEMENTATION: Sequence Starting CountsComplete the code below to estimate the bigram probabilities of a sequence starting with each tag. | def starting_counts(sequences):
"""Return a dictionary keyed to each unique value in the input sequences list
that counts the number of occurrences where that value is at the beginning of
a sequence.
For example, if 8093 sequences start with NOUN, then you should return a
dictionary such that y... | _____no_output_____ | MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
IMPLEMENTATION: Sequence Ending CountsComplete the function below to estimate the bigram probabilities of a sequence ending with each tag. | def ending_counts(sequences):
"""Return a dictionary keyed to each unique value in the input sequences list
that counts the number of occurrences where that value is at the end of
a sequence.
For example, if 18 sequences end with DET, then you should return a
dictionary such that your_starting_... | _____no_output_____ | MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
IMPLEMENTATION: Basic HMM TaggerUse the tag unigrams and bigrams calculated above to construct a hidden Markov tagger.- Add one state per tag - The emission distribution at each state should be estimated with the formula: $P(w|t) = \frac{C(t, w)}{C(t)}$- Add an edge from the starting state `basic_model.start` to ea... | basic_model = HiddenMarkovModel(name="base-hmm-tagger")
states = {}
for tag in emission_counts:
tag_count = tag_unigrams[tag]
prob_distributuion = {word : word_count/tag_count for word, word_count in emission_counts[tag].items() }
state = State(DiscreteDistribution(prob_distributuion), name=tag)
state... | training accuracy basic hmm model: 97.54%
testing accuracy basic hmm model: 96.18%
| MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
Example Decoding Sequences with the HMM Tagger | for key in data.testing_set.keys[:3]:
print("Sentence Key: {}\n".format(key))
print("Predicted labels:\n-----------------")
print(simplify_decoding(data.sentences[key].words, basic_model))
print()
print("Actual labels:\n--------------")
print(data.sentences[key].tags)
print("\n") | Sentence Key: b100-28144
Predicted labels:
-----------------
['CONJ', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'CONJ', 'NOUN', 'NUM', '.', '.', 'NOUN', '.', '.']
Actual labels:
--------------
('CONJ', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'NOUN', 'NUM', '.', 'CONJ', 'NOUN', 'NUM', '.', '.', 'NOUN... | MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
Step 4: [Optional] Improving model performance---There are additional enhancements that can be incorporated into your tagger that improve performance on larger tagsets where the data sparsity problem is more significant. The data sparsity problem arises because the same amount of data split over more tags means there ... | import nltk
from nltk import pos_tag, word_tokenize
from nltk.corpus import brown
nltk.download('brown')
training_corpus = nltk.corpus.brown
training_corpus.tagged_sents()[0] | _____no_output_____ | MIT | HMM TaggerPart of Speech Tagging - HMM.ipynb | Akshat2127/Part-Of-Speech-Tagging |
[Bucles `for`](https://docs.python.org/3/tutorial/controlflow.htmlfor-statements) Iterando listas | mi_lista = [1, 2, 3, 4, 'Python', 'es', 'piola']
for item in mi_lista:
print(item) | _____no_output_____ | MIT | notebooks/beginner/notebooks/for_loops.ipynb | mateodif/learn-python3 |
`break`Parar la ejecución del bucle. | for item in mi_lista:
if item == 'Python':
break
print(item) | _____no_output_____ | MIT | notebooks/beginner/notebooks/for_loops.ipynb | mateodif/learn-python3 |
`continue`Continúa al próximo item sin ejecutar las lineas después de `continue` dentro del bucle. | for item in mi_lista:
if item == 1:
continue
print(item) | _____no_output_____ | MIT | notebooks/beginner/notebooks/for_loops.ipynb | mateodif/learn-python3 |
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