markdown stringlengths 0 37k | code stringlengths 1 33.3k | path stringlengths 8 215 | repo_name stringlengths 6 77 | license stringclasses 15
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Now let’s do a describe and plot it again. | df.plot(x='Miles', y='Minutes', kind='scatter') | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
Let’s plot Miles and Minutes together in a scatter plot. Wow that’s linear. Let’s see how correlated they are. We do this with the cor method. We can see that Miles to time are very tightly correlated (using pearson standard correlation coefficients) there are two other correlation methods that you can use, kendall Tau... | df.corr()
df.corr(method='kendall')
df.corr(method='spearman') | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
Now let’s see a box plot. With these two we get a much better idea of the data. We can see that most of my runs are below an hour except for a couple that are much longer.- | df.boxplot('Minutes', return_type='axes') | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
Now let’s add minutes per mile, we can just divide our two series to get those numbers. | df['Minutes'] / df['Miles']
df['Min_per_mile'] = df['Minutes'] / df['Miles']
df.describe() | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
We can see that along more shorter distances, my speed can vary a lot. | df.plot(x='Miles', y='Min_per_mile', kind='scatter')
plt.ylabel("Minutes / Mile") | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
Let’s see a histogram of my speeds.
Histograms are a great way of representing frequency data or how much certain things are occuring. | df.hist('Min_per_mile') | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
seems pretty center in that 7 minutes to 7.5 minute range. Let’s see if we can get more information with more bins which we specify with the bin argument. | df.hist('Min_per_mile',bins=20) | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
That’s interesting. Under 7 and then at 7.5 are the most popular. I bet that has something to do with my running distances too or the courses I choose to run. | df.hist('Min_per_mile',bins=20, figsize=(10,8))
plt.xlim((5, 11))
plt.ylim((0, 12))
plt.title("Minutes Per Mile Histogram")
plt.grid(False)
plt.savefig('../assets/minutes_per_mile_histogram.png')
df['Miles'] | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
Now another cool thing you can do with time series is see the rolling mean or rolling sum or even rolling correlations. There’s a lot of different “rolling” type things you can do. | df['Miles'].plot() | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
So here’s a standard plot of our Miles again, just a line over time. To add another line to the same plot we just add more details to the box. As I was touching on the rolling values. Let’s talk about the rolling average. Now to do that I pass it a series or a data frame. | df['Miles'].plot()
pd.rolling_mean(df['Miles'], 7).plot() | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
I can do the same with the rolling standard deviation or sum. | df['Miles'].plot()
pd.rolling_std(df['Miles'], 7).plot()
df['Miles'].plot()
pd.rolling_sum(df['Miles'], 7).plot() | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
Now on the last note one thing that’s cool about date time indexes is that you can query them very naturally. If I want to get all my runs in october of 2014, I just enter that as a string. | df.index | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
If I want to get from November to December, I can do that as a Series. | df['2014-11':'2014-12'] | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
How do you think we might go from october to January 1 2015?
Go ahead and give it a try and see if you can figure it out. | df['2014-11':'2015-1-1']['Miles'].plot() | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
Now we can specify a series this way but we can’t specific a specific date. To get a specific date’s run. | df['2014-8-12'] | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
To do that we need to use loc. | df.loc['2014-8-12'] | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
now that we’ve done all this work. We should save it so that we don’t have to remember what our operations were or what stage we did them at. Now we could save it to csv like we did our other one but I wanted to illustrate all the different ways you can save this file.
Let’s save our csv, but we can also save it as an ... | df.head()
df.to_csv('../data/date_fixed_running_data_with_time.csv')
df.to_html('../data/date_fixed_running_data_with_time.html') | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
One thing to note with JSON files is that they want unique indexes (because they're going to be come the keys), so we've got to give it a new index. We can do this by resetting our index or setting our index to a column. | df.to_json('../data/date_fixed_running_data_with_time.json')
df.reset_index()
df['Date'] = df.index
df.index = range(df.shape[0])
df.head()
df.to_json('../data/date_fixed_running_data_with_time.json') | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
Now there’s a LOT more you can do with date time indexing but this is about all that I wanted to cover in this video. We will get into more specifics later. By now you should be getting a lot more familiar with pandas and what the ipython + pandas workflow is. | df.Date[0] | 4 - pandas Basics/4-6 pandas DataFrame Renaming Cols, Handling NaN Values, Maps, Intermediate Plotting, + Rolling Values, + Basic Date Indexing.ipynb | mitchshack/data_analysis_with_python_and_pandas | apache-2.0 |
Homework 14 (or so): TF-IDF text analysis and clustering
Hooray, we kind of figured out how text analysis works! Some of it is still magic, but at least the TF and IDF parts make a little sense. Kind of. Somewhat.
No, just kidding, we're professionals now.
Investigating the Congressional Record
The Congressional Record... | # If you'd like to download it through the command line...
#!curl -O http://www.cs.cornell.edu/home/llee/data/convote/convote_v1.1.tar.gz
# And then extract it through the command line...
#!tar -zxf convote_v1.1.tar.gz | homework13/14 - TF-IDF Homework.ipynb | radhikapc/foundation-homework | mit |
So great, we have 702 of them. Now let's import them. | speeches = []
for path in paths:
with open(path) as speech_file:
speech = {
'pathname': path,
'filename': path.split('/')[-1],
'content': speech_file.read()
}
speeches.append(speech)
speeches_df = pd.DataFrame(speeches)
#speeches_df.head()
speeches_df['pathnam... | homework13/14 - TF-IDF Homework.ipynb | radhikapc/foundation-homework | mit |
In class we had the texts variable. For the homework can just do speeches_df['content'] to get the same sort of list of stuff.
Take a look at the contents of the first 5 speeches | texts =speeches_df['content']
texts[:5] | homework13/14 - TF-IDF Homework.ipynb | radhikapc/foundation-homework | mit |
Doing our analysis
Use the sklearn package and a plain boring CountVectorizer to get a list of all of the tokens used in the speeches. If it won't list them all, that's ok! Make a dataframe with those terms as columns.
Be sure to include English-language stopwords | from sklearn.feature_extraction.text import CountVectorizer
count_vectorizer = CountVectorizer(stop_words='english')
Xc = count_vectorizer.fit_transform(texts)
Xc
Xc.toarray()
pd.DataFrame(Xc.toarray()).head(3)
Xc_feature= pd.DataFrame(Xc.toarray(), columns=count_vectorizer.get_feature_names())
Xc_feature.head(3) | homework13/14 - TF-IDF Homework.ipynb | radhikapc/foundation-homework | mit |
Okay, it's far too big to even look at. Let's try to get a list of features from a new CountVectorizer that only takes the top 100 words. | from nltk.stem.porter import PorterStemmer
porter_stemmer = PorterStemmer()
porter_stemmer = PorterStemmer()
def stemming_tokenizer(str_input):
words = re.sub(r"[^A-Za-z]", " ", str_input).lower().split()
words = [porter_stemmer.stem(word) for word in words]
#print(words)
return words
coun... | homework13/14 - TF-IDF Homework.ipynb | radhikapc/foundation-homework | mit |
Now let's push all of that into a dataframe with nicely named columns. | df_Xc = pd.DataFrame(Xc100.toarray(), columns=count_vectorizer.get_feature_names())
df_Xc.head(3) | homework13/14 - TF-IDF Homework.ipynb | radhikapc/foundation-homework | mit |
Everyone seems to start their speeches with "mr chairman" - how many speeches are there total, and many don't mention "chairman" and how many mention neither "mr" nor "chairman"? | df_Xc['act'].count()
df_Xc[df_Xc["chairman"]==0]['chairman'].count()
df_Xc[df_Xc["mr"]==0]['mr'].count()
total = df_Xc[df_Xc["mr"]==0]['mr'].count() + df_Xc[df_Xc["chairman"]==0]['chairman'].count()
print(total,"speaches in total do not mention neither 'mr' nor 'chairman'") | homework13/14 - TF-IDF Homework.ipynb | radhikapc/foundation-homework | mit |
What is the index of the speech thank is the most thankful, a.k.a. includes the word 'thank' the most times? | thank = df_Xc[df_Xc["thank"]!=0]
thank.head(3)
thank_column = thank['thank']
thank_column.sort(inplace=False, ascending=False).head(1) | homework13/14 - TF-IDF Homework.ipynb | radhikapc/foundation-homework | mit |
If I'm searching for China and trade, what are the top 3 speeches to read according to the CountVectoriser? | china_trade = df_Xc['china'] + df_Xc['trade']
china_trade.sort(inplace=False, ascending=False).head(3) | homework13/14 - TF-IDF Homework.ipynb | radhikapc/foundation-homework | mit |
Now what if I'm using a TfidfVectorizer? | tfidf_vectorizer = TfidfVectorizer(stop_words='english', tokenizer=stemming_tokenizer, use_idf=False, norm='l1', max_features=100)
Xt = tfidf_vectorizer.fit_transform(texts)
pd.DataFrame(Xt.toarray(), columns=tfidf_vectorizer.get_feature_names()).head(3)
print(tfidf_vectorizer.get_feature_names())
# checking inverse t... | homework13/14 - TF-IDF Homework.ipynb | radhikapc/foundation-homework | mit |
What's the content of the speeches? Here's a way to get them: | # index 0 is the first speech, which was the first one imported.
paths[0]
# Pass that into 'cat' using { } which lets you put variables in shell commands
# that way you can pass the path to cat
!echo {paths[0]}
!type a.text | homework13/14 - TF-IDF Homework.ipynb | radhikapc/foundation-homework | mit |
Now search for something else! Another two terms that might show up. elections and chaos? Whatever you thnik might be interesting. | df_Xc.columns
congress_lawsuit = df_Xc['lawsuit'] + df_Xc['congress']
congress_lawsuit.sort(inplace=False, ascending=False).head(5)
pd.DataFrame([df_Xc['lawsuit'], df_Xc['congress'], df_Xc['lawsuit'] + df_Xc['congress']], index=["congress", "lawsuit", "congress + lawsuit"]).T | homework13/14 - TF-IDF Homework.ipynb | radhikapc/foundation-homework | mit |
Enough of this garbage, let's cluster
Using a simple counting vectorizer, cluster the documents into eight categories, telling me what the top terms are per category.
Using a term frequency vectorizer, cluster the documents into eight categories, telling me what the top terms are per category.
Using a term frequency in... | from sklearn.cluster import KMeans
#count vectorization Xc100 is a set of normalized for 100 top words
number_of_clusters = 8
km = KMeans(n_clusters=number_of_clusters)
km.fit(Xc100)
#count vectorization
print("Top terms per cluster:")
order_centroids = km.cluster_centers_.argsort()[:, ::-1]
terms = count_vectorizer... | homework13/14 - TF-IDF Homework.ipynb | radhikapc/foundation-homework | mit |
Which one do you think works the best?
Harry Potter time
I have a scraped collection of Harry Potter fanfiction at https://github.com/ledeprogram/courses/raw/master/algorithms/data/hp.zip.
I want you to read them in, vectorize them and cluster them. Use this process to find out the two types of Harry Potter fanfiction.... | import glob
paths = glob.glob('hp/hp/*')
paths[:5]
len(paths)
reviews = []
for path in paths:
with open(path) as review_file:
review = {
'pathname': path,
'filename': path.split('/')[-1],
'content': review_file.read()
}
reviews.append(review)
reviews_df = pd... | homework13/14 - TF-IDF Homework.ipynb | radhikapc/foundation-homework | mit |
Vectorize
Count Vectorization | from sklearn.feature_extraction.text import CountVectorizer
from nltk.stem.porter import PorterStemmer
porter_stemmer = PorterStemmer()
def stemming_tokenizer(str_input):
words = re.sub(r"[^A-Za-z]", " ", str_input).lower().split()
words = [porter_stemmer.stem(word) for word in words]
#print(words)
... | homework13/14 - TF-IDF Homework.ipynb | radhikapc/foundation-homework | mit |
Time to build the network
Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.
The network has two layers, a hid... | class NeuralNetwork(object):
def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
def sigmoid(x):
#Sigmoid Function
return 1/(1+np.exp(-x))
# Set number of nodes in input, hidden and output layers.
self.input_nodes = input_nod... | dlnd-your-first-neural-network.ipynb | luiscapo/DLND-your-first-neural-network | gpl-3.0 |
Training the network
Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training se... | import sys
### Set the hyperparameters here ###
epochs = 4000
learning_rate = 0.01
hidden_nodes = 30
output_nodes = 1
N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
losses = {'train':[], 'validation':[]}
for e in range(epochs):
# Go through a random batch of... | dlnd-your-first-neural-network.ipynb | luiscapo/DLND-your-first-neural-network | gpl-3.0 |
Thinking about your results
Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?
Note: You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter
Your answer below
With this Pa... | import unittest
inputs = [0.5, -0.2, 0.1]
targets = [0.4]
test_w_i_h = np.array([[0.1, 0.4, -0.3],
[-0.2, 0.5, 0.2]])
test_w_h_o = np.array([[0.3, -0.1]])
class TestMethods(unittest.TestCase):
##########
# Unit tests for data loading
##########
def test_data_path(self... | dlnd-your-first-neural-network.ipynb | luiscapo/DLND-your-first-neural-network | gpl-3.0 |
上面可以初步看到程序本身执行时间很短,大部分时间在等待写什么. 只能看到一个大概,不能定位到具体代码.
contextmanager
使用python的上下文管理器机制对代码进行耗时度量:
- __enter__:记录开始时间
- __exit__: 记录结束时间 | %%writefile timer.py
import time
class Timer(object):
def __init__(self, verbose=False):
self.verbose = verbose
def __enter__(self):
self.start = time.time()
return self
def __exit__(self, *args):
self.end = time.time()
self.secs = self.end - self.start
... | books/optimization/performance-analysis.ipynb | 510908220/python-toolbox | mit |
可以将耗时写到日志里,这样在写代码的时候对关键的逻辑处(数据库、网络等)进行如上改写,然后通过分析日志排查性能问题. 当然也可以扩展一下将每次性能数据写入数据库分析.
line_profiler
line_profiler可以分析每一行代码的执行耗时信息.
为了使用line_profiler,使用pip install line_profiler进行安装. 安装成功后可以看到叫做kernprof的可执行程序.
在使用工具测试代码性能的时候, 需要给函数加上@profile装饰器.(不需要显示import任何模块,kernprof会自动注入的) | %%writefile slow_app_for_profiler.py
import sys
import time
@profile
def mock_download():
for i in range(5):
time.sleep(1)
@profile
def mock_database():
for i in range(20):
time.sleep(0.1)
@profile
def main():
mock_download()
mock_database()
if __name__ == "__main__":
sys.exit(... | books/optimization/performance-analysis.ipynb | 510908220/python-toolbox | mit |
-l选项告诉kernprof注入@profile到脚本里. -v告诉kernprof显示执行结果到控制台.
Line #:行号.
Hits: 这行代码运行次数.
Time: 这一行总耗时
Per Hit: 本行代码执行一次耗时.
% Time:本行耗时占总耗时(函数耗时)百分比.
Line Contents: 代码
从结果可以很清楚的看到每一行的耗时, 这个对于一般的脚本很方便, 但是对于django项目怎么办呢:
- 使用django-devserver: 这个适合在开发环境发现一些性能问题,但是很多问题在线上才能发现. http://djangotricks.blogspot.com/2015/01/performance-... | !pip install memory_profiler psutil
!python -m memory_profiler slow_app_for_profiler.py | books/optimization/performance-analysis.ipynb | 510908220/python-toolbox | mit |
<img src="image/weight_biases.png" style="height: 60%;width: 60%; position: relative; right: 10%">
Problem 2
For the neural network to train on your data, you need the following <a href="https://www.tensorflow.org/resources/dims_types.html#data-types">float32</a> tensors:
- features
- Placeholder tensor for feature ... | features_count = 784
labels_count = 10
# TODO: Set the features and labels tensors
# features =
# labels =
# TODO: Set the weights and biases tensors
# weights =
# biases =
### DON'T MODIFY ANYTHING BELOW ###
#Test Cases
from tensorflow.python.ops.variables import Variable
assert features._op.name.startswith... | Term_1/TensorFlow_3/TensorFlow_Lab/lab.ipynb | akshaybabloo/Car-ND | mit |
These are our observations:
The maximum number of survivors are in the first and third class, respectively
With respect to the total number of passengers in each class, first class has the maximum survivors at around 61%
With respect to the total number of passengers in each class, third class has the minimum number o... | # Checking for any null values
df['Sex'].isnull().value_counts()
# Male passengers survived in each class
male_survivors = df[df['Sex'] == 'male'].groupby('Pclass')['Survived'].agg(sum)
male_survivors
# Total Male Passengers in each class
male_total_passengers = df[df['Sex'] == 'male'].groupby('Pclass')['PassengerId'... | _oldnotebooks/Titanic_Data_Mining.ipynb | eneskemalergin/OldBlog | mit |
These are our observations:
The majority of survivors are females in all the classes
More than 90% of female passengers in first and second class survived
The percentage of male passengers who survived in first and third class, respectively, are comparable
This is our key takeaway:
Female passengers were given pre... | # Checking for the null values
df['SibSp'].isnull().value_counts()
# Checking for the null values
df['Parch'].isnull().value_counts()
# Total number of non-survivors in each class
non_survivors = df[(df['SibSp'] > 0) | (df['Parch'] > 0) & (df['Survived'] == 0)].groupby('Pclass')['Survived'].agg('count')
non_survivors... | _oldnotebooks/Titanic_Data_Mining.ipynb | eneskemalergin/OldBlog | mit |
These are our observations:
There are lot of nonsurvivors in the third class
Second class has the least number of nonsurvivors with relatives
With respect to the total number of passengers, the first class, who had relatives aboard, has the maximum nonsurvivor percentage and the third class has the least
This is our ... | # Checking for null values
df['Age'].isnull().value_counts()
# Defining the age binning interval
age_bin = [0, 18, 25, 40, 60, 100]
# Creating the bins
df['AgeBin'] = pd.cut(df.Age, bins=age_bin)
d_temp = df[np.isfinite(df['Age'])]
# Number of survivors based on Age bin
survivors = d_temp.groupby('AgeBin')['Survived... | _oldnotebooks/Titanic_Data_Mining.ipynb | eneskemalergin/OldBlog | mit |
Load Dataset | IB = pd.read_csv("india-batting.csv")
IB.head(5)
IB.columns | India’s_batting_performance_2016.ipynb | erayon/India-Australia-Cricket-Analysis | gpl-3.0 |
Split the year from 'Start Date' columns and create a new column name 'year' | year=[]
for i in range(len(IB)):
x = IB['Start Date'][i].split(" ")[-1]
year.append(x)
year= pd.DataFrame(year,columns=["year"])
mr = [IB,year]
df=pd.concat(mr,axis=1)
df.head(5) | India’s_batting_performance_2016.ipynb | erayon/India-Australia-Cricket-Analysis | gpl-3.0 |
Find all rows in the year 2016 from new dataframe and remove 'DND' rows from the dataframe which appear in the 'Runs' columns | df_16 = df[df["year"]=="2016"]
df_16=df_16.reset_index(drop=True)
df_16.columns
Runs = np.array(df_16["Runs"])
np.squeeze(np.where(Runs=="DNB"))
ndf_16=df_16[0:88]
ndf_16.head(5) | India’s_batting_performance_2016.ipynb | erayon/India-Australia-Cricket-Analysis | gpl-3.0 |
Create a Dateframe of unique players name and their maximum score | ndf_16.Player.unique()
playernames = ndf_16.Player.unique()
runs=[]
for i in range(len(ndf_16)):
try:
r = np.int(ndf_16['Runs'][i])
except:
r= np.int(ndf_16.Runs.unique()[0].split("*")[0])
runs.append(r)
modRun = pd.DataFrame(runs,columns=["modRun"])
modDf = pd.concat([ndf_16,modRun],axis... | India’s_batting_performance_2016.ipynb | erayon/India-Australia-Cricket-Analysis | gpl-3.0 |
Visulaize in Plotly | import plotly
plotly.tools.set_credentials_file(username='ayon.mi1', api_key='iIBYMNu0RVcR1GmQSeD0')
data = [go.Bar(
x=np.array(dfx['player_name']),
y=np.array(dfx['max_run'])
)]
layout = go.Layout(
title='Maximun_Score per player',
xaxis=dict(
title='Pla... | India’s_batting_performance_2016.ipynb | erayon/India-Australia-Cricket-Analysis | gpl-3.0 |
Expected output:
<table>
<tr>
<td>
**gradients["dWaa"][1][2] **
</td>
<td>
10.0
</td>
</tr>
<tr>
<td>
**gradients["dWax"][3][1]**
</td>
<td>
-10.0
</td>
</td>
</tr>
<tr>
<td>
**gradients["dWya"][1][2]**
</td>
<td>
0.29713815361
</td>
</tr>
<... | # GRADED FUNCTION: sample
def sample(parameters, char_to_ix, seed):
"""
Sample a sequence of characters according to a sequence of probability distributions output of the RNN
Arguments:
parameters -- python dictionary containing the parameters Waa, Wax, Wya, by, and b.
char_to_ix -- python dictio... | coursera/deep-neural-network/quiz and assignments/RNN/Dinosaurus+Island+--+Character+level+language+model+final+-+v3.ipynb | jinntrance/MOOC | cc0-1.0 |
Time to build the network
Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.
<img src="assets/neural_network.p... | class NeuralNetwork(object):
def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
# Set number of nodes in input, hidden and output layers.
self.input_nodes = input_nodes
self.hidden_nodes = hidden_nodes
self.output_nodes = output_nodes
# Initialize we... | first-neural-network/Your_first_neural_network.ipynb | tanmay987/deepLearning | mit |
Training the network
Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training se... | import sys
### Set the hyperparameters here ###
iterations = 5000
learning_rate = 0.5
hidden_nodes = 30
output_nodes = 1
N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
losses = {'train':[], 'validation':[]}
for ii in range(iterations):
# Go through a random ... | first-neural-network/Your_first_neural_network.ipynb | tanmay987/deepLearning | mit |
<a id='wrangling'></a>
Data Wrangling
General Properties | # Load TMDb data and print out a few lines. Perform operations to inspect data
# types and look for instances of missing or possibly errant data.
tmdb_movies = pd.read_csv('tmdb-movies.csv')
tmdb_movies.head()
tmdb_movies.describe() | mlfoundation/istat/project/investigate-a-dataset-template.ipynb | vikashvverma/machine-learning | mit |
Data Cleaning
As evident from the data, it seems we have cast of the movie as string separated by | symbol. This needs to be converted into a suitable type in order to consume it properly later. | # Pandas read empty string value as nan, make it empty string
tmdb_movies.cast.fillna('', inplace=True)
tmdb_movies.genres.fillna('', inplace=True)
tmdb_movies.director.fillna('', inplace=True)
tmdb_movies.production_companies.fillna('', inplace=True)
def string_to_array(data):
"""
This function returns gi... | mlfoundation/istat/project/investigate-a-dataset-template.ipynb | vikashvverma/machine-learning | mit |
Convert cast, genres, director and production_companies columns to array | tmdb_movies.cast = tmdb_movies.cast.apply(string_to_array)
tmdb_movies.genres = tmdb_movies.genres.apply(string_to_array)
tmdb_movies.director = tmdb_movies.director.apply(string_to_array)
tmdb_movies.production_companies = tmdb_movies.production_companies.apply(string_to_array) | mlfoundation/istat/project/investigate-a-dataset-template.ipynb | vikashvverma/machine-learning | mit |
<a id='eda'></a>
Exploratory Data Analysis
Research Question 1: What is the yearly revenue change?
It's evident from observations below that there is no clear trend in change in mean revenue over years.
Mean revenue from year to year is quite unstable. This can be attributed to number of movies and number of movies h... | def yearly_growth(mean_revenue):
return mean_revenue - mean_revenue.shift(1).fillna(0)
# Show change in mean revenue over years, considering only movies for which we have revenue data
movies_with_budget = tmdb_movies[tmdb_movies.budget_adj > 0]
movies_with_revenue = movies_with_budget[movies_with_budget.revenue_ad... | mlfoundation/istat/project/investigate-a-dataset-template.ipynb | vikashvverma/machine-learning | mit |
Research Question 2: Which genres are most popular from year to year?
Since popularity column indicates all time popularity of the movie, it might not be the right metric to measure popularity over years. We can measure popularty of a movie based on average vote. I think a movie is popular if vote_average >= 7.
On a... | def popular_movies(movies):
return movies[movies['vote_average']>=7]
def group_by_genre(data):
"""
This function takes a Data Frame having and returns a dictionary having
release_year as key and value a dictionary having key as movie's genre
and value as frequency of the genre that year... | mlfoundation/istat/project/investigate-a-dataset-template.ipynb | vikashvverma/machine-learning | mit |
Research Question 3: What kinds of properties are associated with movies that have high revenues?
We can consider those movies with at least 1 billion revenue and see what are common properties among them.
Considering this criteria and based on illustrations below, we can make following observations about highest gross... | highest_grossing_movies = tmdb_movies[tmdb_movies['revenue_adj'] >= 1000000000]\
.sort_values(by='revenue_adj', ascending=False)
highest_grossing_movies.head() | mlfoundation/istat/project/investigate-a-dataset-template.ipynb | vikashvverma/machine-learning | mit |
Find common genres in highest grossing movies | def count_frequency(data):
frequency_count = {}
for items in data:
for item in items:
if item in frequency_count:
frequency_count[item] += 1
else:
frequency_count[item] = 1
return frequency_count
highest_grossing_genres = count_frequency(highe... | mlfoundation/istat/project/investigate-a-dataset-template.ipynb | vikashvverma/machine-learning | mit |
Popularity of highest grossing movies | highest_grossing_movies.vote_average.hist() | mlfoundation/istat/project/investigate-a-dataset-template.ipynb | vikashvverma/machine-learning | mit |
Directors of highest grossing movies | def list_to_dict(data, label):
"""
This function creates returns statistics and indices for a data frame
from a list having label and value.
"""
statistics = {label: []}
index = []
for item in data:
statistics[label].append(item[1])
index.append(item[0])
return st... | mlfoundation/istat/project/investigate-a-dataset-template.ipynb | vikashvverma/machine-learning | mit |
Cast of highest grossing movies | high_grossing_cast = count_frequency(highest_grossing_movies.cast)
revenues, index = list_to_dict(sorted(high_grossing_cast.items(), key=operator.itemgetter(1), reverse=True)[:30], 'number of movies')
pd.DataFrame(revenues, index=index).plot(kind='bar', figsize=(20, 5)) | mlfoundation/istat/project/investigate-a-dataset-template.ipynb | vikashvverma/machine-learning | mit |
Production companies of highest grossing movies | high_grossing_prod_comps = count_frequency(highest_grossing_movies.production_companies)
revenues, index = list_to_dict(sorted(high_grossing_prod_comps.items(), key=operator.itemgetter(1), reverse=True)[:30]\
, 'number of movies')
pd.DataFrame(revenues, index=index).plot(kind='bar', f... | mlfoundation/istat/project/investigate-a-dataset-template.ipynb | vikashvverma/machine-learning | mit |
Highest grossing budget
Research Question 4: Who are top 15 highest grossing directors?
We can see the top 30 highest grossing directors in bar chart below.
It seems Steven Spielberg surpasses other directors in gross revenue. | def grossing(movies, by):
"""
This function returns the movies' revenues over key passed as `by` value in argument.
"""
revenues = {}
for id, movie in movies.iterrows():
for key in movie[by]:
if key in revenues:
revenues[key].append(movie.revenue_adj)
... | mlfoundation/istat/project/investigate-a-dataset-template.ipynb | vikashvverma/machine-learning | mit |
Research Question 5: Who are top 15 highest grossing actors?
We can find the top 30 actors based on gross revenue as shown in subsequent sections below.
As we can see Harison Ford tops the chart with highest grossing. | gross_by_actors = grossing(movies=tmdb_movies, by='cast')
actors_gross_revenue = gross_revenue(gross_by_actors)
top_15_actors = sorted(actors_gross_revenue.items(), key=operator.itemgetter(1), reverse=True)[:15]
revenues, indexes = list_to_dict(top_15_actors, 'actors')
pd.DataFrame(data=revenues, index=indexes).plot(k... | mlfoundation/istat/project/investigate-a-dataset-template.ipynb | vikashvverma/machine-learning | mit |
Step 1: Truncate the series to the interval that has observations. Outside this interval the interpolation blows up. | print('Original bounds: ', t[0], t[-1])
t_obs = t[D['T_flag'] != -1]
D = D[t_obs[0]:t_obs[-1]] # Truncate dataframe so it is sandwiched between observed values
t = D.index
T = D['T']
print('New bounds: ', t[0], t[-1])
t_obs = D.index[D['T_flag'] != -1]
t_interp = D.index[D['T_flag'] == -1]
T_obs = D.loc[t_obs, 'T']
... | notebooks/clean_data.ipynb | RJTK/dwglasso_cweeds | mit |
Red dots are interpolated values. | # Centre the data
mu = D['T'].mean()
D.loc[:, 'T'] = D.loc[:, 'T'] - mu
T = D['T']
print('E[T] = ', mu) | notebooks/clean_data.ipynb | RJTK/dwglasso_cweeds | mit |
We want to obtain a stationary "feature" from the data, firt differences are an easy place to start. | T0 = T[0]
dT = T.diff()
dT = dT - dT.mean() # Center the differences
dT_obs = dT[t_obs]
dT_interp = dT[t_interp]
plt.scatter(t, dT, marker = '.', alpha = 0.5, s = 0.5, c = c)
#obs = plt.scatter(t_obs, dT_obs, marker = '.', alpha = 0.5, s = 0.5, color = 'blue');
#interp = plt.scatter(t_interp, dT_interp, marker = '.'... | notebooks/clean_data.ipynb | RJTK/dwglasso_cweeds | mit |
It appears that early temperature sensors had rather imprecise readings.
It also appears as though the interpolation introduces some systematic errors. I used pchip interpolation, which tries to avoid overshoot, so we may be seeing the effects of clipping. This would particularly make sense if missing data was from r... | rolling1w_dT = dT.rolling(window = 7*24) # 1 week rolling window of dT
rolling1m_dT = dT.rolling(window = 30*24) # 1 month rolling window of dT
rolling1y_dT = dT.rolling(window = 365*24) # 1 year rolling dindow of dT
fig, axes = plt.subplots(3, 1)
axes[0].plot(rolling1w_dT.var())
axes[1].plot(rolling1m_dT.var())
ax... | notebooks/clean_data.ipynb | RJTK/dwglasso_cweeds | mit |
It looks like there is still some nonstationarity in the first differences. | from itertools import product
t_days = [t[np.logical_and(t.month == m, t.day == d)] for m, d in product(range(1,13), range(1, 32))]
day_vars = pd.Series(dT[ti].var() for ti in t_days)
day_vars = day_vars.dropna()
plt.scatter(day_vars.index, day_vars)
r = day_vars.rolling(window = 20, center = True)
plt.plot(day_vars.i... | notebooks/clean_data.ipynb | RJTK/dwglasso_cweeds | mit |
Generating equations for fully contracted terms
In the previous notebook, we computed the coupled cluster energy expression
\begin{equation}
E = \langle \Phi | e^{-\hat{T}} \hat{H} e^{\hat{T}} | \Phi \rangle
= E_0 + \sum_{i}^\mathbb{O} \sum_{a}^\mathbb{V} f^{a}{i} t^{i}{a} +
\frac{1}{4} \sum_{ij}^\mathbb{O} \sum_{ab}^... | E0 = w.op("E_0",[""])
F = w.utils.gen_op('f',1,'ov','ov')
V = w.utils.gen_op('v',2,'ov','ov')
H = E0 + F + V
T = w.op("t",["v+ o", "v+ v+ o o"])
Hbar = w.bch_series(H,T,2)
expr = wt.contract(Hbar,0,0)
expr | tutorials/04-GeneratingCode.ipynb | fevangelista/wicked | mit |
First we convert the expression derived into a set of equations. You get back a dictionary that shows all the components to the equations. The vertical bar (|) in the key separates the lower (left) and upper (right) indices in the resulting expression | mbeq = expr.to_manybody_equations('r')
mbeq | tutorials/04-GeneratingCode.ipynb | fevangelista/wicked | mit |
Converting equations to code
From the equations generated above, you can get tensor contractions by calling the compile function on each individual term in the equations. Here we generate python code that uses numpy's einsum function to evaluate contractions. To use this code you will need to import einsum
python
from ... | for eq in mbeq['|']:
print(eq.compile('einsum')) | tutorials/04-GeneratingCode.ipynb | fevangelista/wicked | mit |
Many-body equations
Suppose we want to compute the contributions to the coupled cluster residual equations
\begin{equation}
r^{i}{a} = \langle \Phi| { \hat{a}^\dagger{i} \hat{a}a } [\hat{F},\hat{T}_1] | \Phi \rangle
\end{equation}
Wick&d can compute this quantity using the corresponding many-body representation of the ... | F = w.utils.gen_op('f',1,'ov','ov')
T1 = w.op("t",["v+ o"])
expr = wt.contract(w.commutator(F,T1),2,2)
latex(expr) | tutorials/04-GeneratingCode.ipynb | fevangelista/wicked | mit |
Next, we call to_manybody_equations to generate many-body equations | mbeq = expr.to_manybody_equations('g')
print(mbeq) | tutorials/04-GeneratingCode.ipynb | fevangelista/wicked | mit |
Out of all the terms, we select the terms that multiply the excitation operator ${ \hat{a}^\dagger_{a} \hat{a}_i }$ ("o|v") | mbeq_ov = mbeq["o|v"]
for eq in mbeq_ov:
latex(eq) | tutorials/04-GeneratingCode.ipynb | fevangelista/wicked | mit |
Lastly, we can compile these equations into code | for eq in mbeq_ov:
print(eq.compile('einsum')) | tutorials/04-GeneratingCode.ipynb | fevangelista/wicked | mit |
Antisymmetrization of uncontracted operator indices
To gain efficiency, Wick&d treats contractions involving inequivalent lines in a special way. Consider the following term contributing to the CCSD doubles amplitude equations that arises from $[\hat{V}\mathrm{ovov},\hat{T}_2]$ (see the sixth term in Eq. (153) of Crawf... | T2 = w.op("t", ["v+ v+ o o"])
Vovov = w.op("v", ["o+ v+ v o"])
expr = wt.contract(w.commutator(Vovov, T2), 4, 4)
latex(expr) | tutorials/04-GeneratingCode.ipynb | fevangelista/wicked | mit |
In wick&d the two-body part of $[\hat{V}\mathrm{ovov},\hat{T}_2]$ gives us only a single term
\begin{equation}
[\hat{V}\mathrm{ovov},\hat{T}2]\text{2-body} = - \sum_{abcijk} \langle kb \| jc \rangle t^{ik}{ac} { \hat{a}^{ab}{ij} } = \sum_{abij} g^{ij}{ab} { \hat{a}^{ab}{ij} }
\end{equation}
where the tensor $g^{ij}{ab... | for eq in expr.to_manybody_equations('g')['oo|vv']:
print(eq.compile('einsum')) | tutorials/04-GeneratingCode.ipynb | fevangelista/wicked | mit |
Experimental Options
The Options class allows the download of options data from Google Finance.
The get_options_data method downloads options data for specified expiry date and provides a formatted DataFrame with a hierarchical index, so its easy to get to the specific option you want.
Available expiry dates can be acc... | from pandas_datareader.data import Options
fb_options = Options('FB', 'google')
data = fb_options.get_options_data(expiry = fb_options.expiry_dates[0])
data.head() | 17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/06-Data-Sources/01 - Pandas-Datareader.ipynb | arcyfelix/Courses | apache-2.0 |
FRED | import pandas_datareader.data as web
import datetime
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2017, 1, 1)
gdp = web.DataReader("GDP", "fred", start, end)
gdp.head() | 17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/06-Data-Sources/01 - Pandas-Datareader.ipynb | arcyfelix/Courses | apache-2.0 |
Split into training and testing
Next we split the data into training and testing data sets | (training, test) = ratingsRDD.randomSplit([0.8, 0.2])
numTraining = training.count()
numTest = test.count()
# verify row counts for each dataset
print("Total: {0}, Training: {1}, test: {2}".format(ratingsRDD.count(), numTraining, numTest)) | notebooks/Step 04 - Realtime Recommendations.ipynb | snowch/movie-recommender-demo | apache-2.0 |
Build the recommendation model using ALS on the training data
I've chosen some values for the ALS parameters. You should probaly experiment with different values. | from pyspark.mllib.recommendation import ALS
rank = 50
numIterations = 20
lambdaParam = 0.1
model = ALS.train(training, rank, numIterations, lambdaParam) | notebooks/Step 04 - Realtime Recommendations.ipynb | snowch/movie-recommender-demo | apache-2.0 |
Extract the product (movie) features | import numpy as np
pf = model.productFeatures().cache()
pf_keys = pf.sortByKey().keys().collect()
pf_vals = pf.sortByKey().map(lambda x: list(x[1])).collect()
Vt = np.matrix(np.asarray(pf.values().collect())) | notebooks/Step 04 - Realtime Recommendations.ipynb | snowch/movie-recommender-demo | apache-2.0 |
Simulate a new user rating a movie | full_u = np.zeros(len(pf_keys))
full_u.itemset(1, 5) # user has rated product_id:1 = 5
recommendations = full_u*Vt*Vt.T
print("predicted rating value", np.sort(recommendations)[:,-10:])
top_ten_recommended_product_ids = np.where(recommendations >= np.sort(recommendations)[:,-10:].min())[1]
print("predict rating prod... | notebooks/Step 04 - Realtime Recommendations.ipynb | snowch/movie-recommender-demo | apache-2.0 |
Volume Distribution
The volume distribution function $V(\sigma_0)$ is normalized by the bin size, giving a results that is independent of the choice of density bin spacing. | def calc_volume(ds, rholevs, zrange=slice(0,-6000)):
vol = ds.HFacC * ds.drF * ds.rA
delta_rho = rholevs[1] - rholevs[0]
ds['volume_rho'] = xgcm.regrid_vertical(vol.sel(Z=zrange),
ds.TRAC01[0].sel(Z=zrange),
rholevs, 'Z') / delta_rho
for ... | MITgcm_WOA13_mixing.ipynb | rabernat/mitgcm-xray | mit |
Cumulative Distribution
By integrating
$$ \int_{\sigma_{min}}^\sigma V(\sigma) d\sigma $$
we obtain the cumulative distribution function. | fig = plt.figure(figsize=(14,6))
ax = fig.add_subplot(111)
for k in atlases:
delta_rho = rholevs[1] - rholevs[0]
ds = dsets[k]
vol_net = ds.volume_rho.sel(Y=slice(-80,-30)).sum(dim=('X','Y'))
vol_cum = vol_net.values.cumsum(axis=0)
plt.plot(rholevs[1:], vol_cum, '.-')
plt.legend(atlases,... | MITgcm_WOA13_mixing.ipynb | rabernat/mitgcm-xray | mit |
Vertical Diffusive Fluxes | rk_sign = -1
plt.figure(figsize=(14,6))
for n, (name, k) in enumerate(zip(
['THETA', 'SALT', 'SIGMA0'],
['_TH', '_SLT', 'Tr01'])):
ax = plt.subplot(1,3,n+1)
for aname in dsets:
ds = dsets[aname]
net_vflux = rk_sign*(
ds['DFrI' + k]... | MITgcm_WOA13_mixing.ipynb | rabernat/mitgcm-xray | mit |
Vertical Diffusive Heat Flux | import gsw
rho0 = 1030
plt.figure(figsize=(4.2,6))
k = '_TH'
ax = plt.subplot(111)
for aname in dsets:
ds = dsets[aname]
net_vflux = rk_sign*rho0*gsw.cp0*(
ds['DFrI' + k] + ds['DFrE' + k]
)[0].sel(Y=slice(-80,-30)).sum(dim=('X','Y'))
plt.plot(net_vflux/1e12, net_vflux.... | MITgcm_WOA13_mixing.ipynb | rabernat/mitgcm-xray | mit |
Symmetric Difference
https://www.hackerrank.com/challenges/symmetric-difference/problem
Task
Given sets of integers, and , print their symmetric difference in ascending order. The term symmetric difference indicates those values that exist in either or but do not exist in both.
Input Format
The first line of input ... | M = int(input())
m =set((map(int,input().split())))
N = int(input())
n =set((map(int,input().split())))
m ^ n
S='add 5 6'
method, *args = S.split()
print(method)
print(*map(int,args))
method,(*map(int,args))
# methods
# (*map(int,args))
# command='add'.split()
# method, args = command[0], list(map(int,command[1:]))... | coding/hacker rank.ipynb | vadim-ivlev/STUDY | mit |
Load house value vs. crime rate data
Dataset is from Philadelphia, PA and includes average house sales price in a number of neighborhoods. The attributes of each neighborhood we have include the crime rate ('CrimeRate'), miles from Center City ('MilesPhila'), town name ('Name'), and county name ('County'). | regressionDir = '/home/weenkus/workspace/Machine Learning - University of Washington/Regression'
sales = pa.read_csv(regressionDir + '/datasets/Philadelphia_Crime_Rate_noNA.csv')
sales
# Show plots in jupyter
%matplotlib inline | Regression/assignments/Simple Linear Regression slides.ipynb | Weenkus/Machine-Learning-University-of-Washington | mit |
Exploring the data
The house price in a town is correlated with the crime rate of that town. Low crime towns tend to be associated with higher house prices and vice versa. | plt.scatter(sales.CrimeRate, sales.HousePrice, alpha=0.5)
plt.ylabel('House price')
plt.xlabel('Crime rate') | Regression/assignments/Simple Linear Regression slides.ipynb | Weenkus/Machine-Learning-University-of-Washington | mit |
Fit the regression model using crime as the feature | # Check the type and shape
X = sales[['CrimeRate']]
print (type(X))
print (X.shape)
y = sales['HousePrice']
print (type(y))
print (y.shape)
crime_model = linear_model.LinearRegression()
crime_model.fit(X, y) | Regression/assignments/Simple Linear Regression slides.ipynb | Weenkus/Machine-Learning-University-of-Washington | mit |
Let's see what our fit looks like | plt.plot(sales.CrimeRate, sales.HousePrice, '.',
X, crime_model.predict(X), '-',
linewidth=3)
plt.ylabel('House price')
plt.xlabel('Crime rate') | Regression/assignments/Simple Linear Regression slides.ipynb | Weenkus/Machine-Learning-University-of-Washington | mit |
Remove Center City and redo the analysis
Center City is the one observation with an extremely high crime rate, yet house prices are not very low. This point does not follow the trend of the rest of the data very well. A question is how much including Center City is influencing our fit on the other datapoints. Let's rem... | sales_noCC = sales[sales['MilesPhila'] != 0.0]
plt.scatter(sales_noCC.CrimeRate, sales_noCC.HousePrice, alpha=0.5)
plt.ylabel('House price')
plt.xlabel('Crime rate')
crime_model_noCC = linear_model.LinearRegression()
crime_model_noCC.fit(sales_noCC[['CrimeRate']], sales_noCC['HousePrice'])
plt.plot(sales_noCC.Crime... | Regression/assignments/Simple Linear Regression slides.ipynb | Weenkus/Machine-Learning-University-of-Washington | mit |
Compare coefficients for full-data fit versus no-Center-City fit¶
Visually, the fit seems different, but let's quantify this by examining the estimated coefficients of our original fit and that of the modified dataset with Center City removed. | print ('slope: ', crime_model.coef_)
print ('intercept: ', crime_model.intercept_)
print ('slope: ', crime_model_noCC.coef_)
print ('intercept: ', crime_model_noCC.intercept_) | Regression/assignments/Simple Linear Regression slides.ipynb | Weenkus/Machine-Learning-University-of-Washington | mit |
Above: We see that for the "no Center City" version, per unit increase in crime, the predicted decrease in house prices is 2,287. In contrast, for the original dataset, the drop is only 576 per unit increase in crime. This is significantly different!
High leverage points:
Center City is said to be a "high leverage" p... | sales_nohighend = sales_noCC[sales_noCC['HousePrice'] < 350000]
crime_model_nohighhend = linear_model.LinearRegression()
crime_model_nohighhend.fit(sales_nohighend[['CrimeRate']], sales_nohighend['HousePrice'])
plt.plot(sales_nohighend.CrimeRate, sales_nohighend.HousePrice, '.',
sales_nohighend[['CrimeRate']], cr... | Regression/assignments/Simple Linear Regression slides.ipynb | Weenkus/Machine-Learning-University-of-Washington | mit |
Do the coefficients change much? | print ('slope: ', crime_model_noCC.coef_)
print ('intercept: ', crime_model_noCC.intercept_)
print ('slope: ', crime_model_nohighhend.coef_)
print ('intercept: ', crime_model_nohighhend.intercept_) | Regression/assignments/Simple Linear Regression slides.ipynb | Weenkus/Machine-Learning-University-of-Washington | mit |
Reading in data to a dataframe
For 1D analysis, we are generally thinking about data that varies in time, so time series analysis. The pandas package is particularly suited to deal with this type of data, having very convenient methods for interpreting, searching through, and using time representations.
Let's start wit... | df = pd.read_csv('../data/yellow_tripdata_2016-05-01_decimated.csv', parse_dates=[0, 2], index_col=[0]) | materials/4_pandas.ipynb | hetland/python4geosciences | mit |
What do all these (and other) input keyword arguments do?
header: tells which row of the data file is the header, from which it will extract column names
parse_dates: try to interpret the values in [col] or [[col1, col2]] as dates, to convert them into datetime objects.
index_col: if no index column is given, an index... | df.index | materials/4_pandas.ipynb | hetland/python4geosciences | mit |
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