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 |
|---|---|---|---|---|---|
Output Table: | orbit_df.head() | _____no_output_____ | Apache-2.0 | notebooks/Orbit Computation/Orbit Computation.ipynb | open-space-collective/open-space-toolk |
2D plot, over **World Map**: | figure = go.Figure(
data = go.Scattergeo(
lon = orbit_df['$Longitude$'],
lat = orbit_df['$Latitude$'],
mode = 'lines',
line = go.scattergeo.Line(
width = 1,
color = 'red'
)
),
layout = go.Layout(
title = None,
showlegend = False... | _____no_output_____ | Apache-2.0 | notebooks/Orbit Computation/Orbit Computation.ipynb | open-space-collective/open-space-toolk |
3D plot, in **Earth Fixed** frame: | figure = go.Figure(
data = [
go.Scattergeo(
lon = orbit_df['$Longitude$'],
lat = orbit_df['$Latitude$'],
mode = 'lines',
line = go.scattergeo.Line(
width = 2,
color = 'rgb(255, 62, 79)'
)
)
],
layout ... | _____no_output_____ | Apache-2.0 | notebooks/Orbit Computation/Orbit Computation.ipynb | open-space-collective/open-space-toolk |
3D plot, in **Earth Inertial** frame: | theta = np.linspace(0, 2 * np.pi, 30)
phi = np.linspace(0, np.pi, 30)
theta_grid, phi_grid = np.meshgrid(theta, phi)
r = float(Earth.equatorial_radius.in_meters())
x = r * np.cos(theta_grid) * np.sin(phi_grid)
y = r * np.sin(theta_grid) * np.sin(phi_grid)
z = r * np.cos(phi_grid)
earth = go.Surface(
x=x,
y=... | _____no_output_____ | Apache-2.0 | notebooks/Orbit Computation/Orbit Computation.ipynb | open-space-collective/open-space-toolk |
prepared by Maksim Dimitrijev (QLatvia) This cell contains some macros. If there is a problem with displaying mathematical formulas, please run this cell to load these macros. $ \newcommand{\bra}[1]{\langle 1|} $$ \newcommand{\ket}[1]{|1\rangle} $$ \newcommand{... | #
# your solution is here
#
| _____no_output_____ | Apache-2.0 | silver/C05_Global_And_Local_Phase.ipynb | VGGatGitHub/QWorld-silver |
Germany: LK Kitzingen (Bayern)* Homepage of project: https://oscovida.github.io* [Execute this Jupyter Notebook using myBinder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/Germany-Bayern-LK-Kitzingen.ipynb) | import datetime
import time
start = datetime.datetime.now()
print(f"Notebook executed on: {start.strftime('%d/%m/%Y %H:%M:%S%Z')} {time.tzname[time.daylight]}")
%config InlineBackend.figure_formats = ['svg']
from oscovida import *
overview(country="Germany", subregion="LK Kitzingen");
# load the data
cases, deaths, re... | _____no_output_____ | CC-BY-4.0 | ipynb/Germany-Bayern-LK-Kitzingen.ipynb | RobertRosca/oscovida.github.io |
Explore the data in your web browser- If you want to execute this notebook, [click here to use myBinder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/Germany-Bayern-LK-Kitzingen.ipynb)- and wait (~1 to 2 minutes)- Then press SHIFT+RETURN to advance code cell to code cell- See http://jupyter.org for... | print(f"Download of data from Johns Hopkins university: cases at {fetch_cases_last_execution()} and "
f"deaths at {fetch_deaths_last_execution()}.")
# to force a fresh download of data, run "clear_cache()"
print(f"Notebook execution took: {datetime.datetime.now()-start}")
| _____no_output_____ | CC-BY-4.0 | ipynb/Germany-Bayern-LK-Kitzingen.ipynb | RobertRosca/oscovida.github.io |
test_str="xxup copy of course appear of course or half - dressed on his gf ill cross ! i was unemployed vans - land through the smallest shoes leave"
for x in test_str.split():
print (f'{x}') | xxup
copy
of
course
appear
of
course
or
half
-
dressed
on
his
gf
ill
cross
!
i
was
unemployed
vans
-
land
through
the
smallest
shoes
leave
| MIT | untoken.ipynb | gdoteof/DiscordChatExporter | |
xxup is a token which mean the next word should be uppercased. | pieces = test_str.split()
for idx,val in enumerate(pieces):
print(f'{idx}:{val}')
def un_xxup(str):
pieces = test_str.split()
for index,value in enumerate(pieces):
if value == 'xxup':
pieces[index+1] = pieces[index+1].capitalize()
del pieces[index]
return " ".join(pieces)
un_xxup(test_str)
| _____no_output_____ | MIT | untoken.ipynb | gdoteof/DiscordChatExporter |
TD1: Timeseries analysis using autoregressive methods and general Box-Jenkins methodsSome useful translations, just in case:- **a timeseries**: une série temporelle (always plural in English)- **a trend**: une tendance- **a lag**: un retard, un décalage dans le temps- **stationary**: stationnaireSome interesting cont... | !pip install statsmodels==0.12.1
!pip install sktime
import requests
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels
import sktime
import scipy | _____no_output_____ | CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
1. AnalysisFor this exercise, we will use a timeseries representing daily average temperature in Melbourne, Australia between 1980 and 1990.This timeseries will be stored in a [Pandas DataFrame object](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html), a standard to handle tabular data ... | # Read data from remote repository
df = pd.read_csv("https://raw.githubusercontent.com/jbrownlee/Datasets/master/daily-min-temperatures.csv", index_col=0)
# Display the 5 first data points
df.head() | _____no_output_____ | CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
1.1 Run-plots analysis"Run-plots" are the simplest representation of a timeseries, where the x-axis represents time and the y-axis represents the observed variable, here temperature in Celsius degrees.**Question: Given the figures and the statistic test below, what hypothesis can you draw regarding the behaviour of th... | # Plot the full timeseries
df.plot(figsize=(20, 4), title="Temperature in Melbourne - 1980 to 1990")
# Plot the first year of data
df.iloc[:365].plot(figsize=(20, 4), title="Temperature in Melbourne - one year") | _____no_output_____ | CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
The Augmented Dickey-Fuller test is a statistical test used to checkthe stationarity of a timeseries. It is implemented in the `adfuller()` function in `statsmodels`. | from statsmodels.tsa.stattools import adfuller
adf, p, *other_stuff = adfuller(df)
print(f"p-value (95% confidence interval): {p:g}, statistics: {adf:g}") | p-value (95% confidence interval): 0.000247083, statistics: -4.4448
| CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
1.2 Autocorrelation and partial autocorrelationAutocorrelation (and partial autocorrelation) are metrics that can be computed to evaluate **how dependent the variable is from its $n$ previous values**, what is called a **lag (of length n)**.**Question: Plot $X[t-1]$ versus $X[t]$, for all $t$. What can you conclude on... | # Create a shifted version of the timeseries:
df_shifted = df.shift(periods=1)
# Plot df vs. df_shifted
plt.figure(figsize=(5, 5))
plt.scatter(df, df_shifted)
plt.xlabel("X[t]")
plt.ylabel("X[t-1]")
plt.show() | _____no_output_____ | CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
It seems to be like X[t] timeseries explains globaly the X[t-1] timeseries because the graph above is quite like a linear graph. But in middle of the graph, the line is very thick so it's difficult to explain the X[t-1] by the X[t] value. ~ not high correlated **Pearson correlation coefficient**To compute this coeffic... | # Plot of the distribution of the variable
# (in our case, the temperature histogram)
df.hist()
# The hist graph seems to be an normal dist hist with a ~ mean of 12
from scipy import stats
# Normality test
k2, p = scipy.stats.normaltest(df)
print(f"p-value (95% confidence interval): {p[0]:g}, statistics: {k2[0]:g}")... | _____no_output_____ | CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
We can see that the value has a quite normal distribution so we can calculate the Pearson Correlation Value. The correlation between X[t-1] and X[t] is 0.77 (~ close to 1) so the two var are very correlated. We probably explain X[t] by X[t-1]. ---We will now compute autocorrelation function (ACF) and partial autocorrel... | from statsmodels.graphics.tsaplots import plot_pacf, plot_acf | _____no_output_____ | CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
1.2.1 Autocorrelation | # Plot autocorrelation for lags between 1 and 730 days
plot_acf(df.values.squeeze(), lags=730)
plt.show()
# Plot autocorrelation for lags between 1 and 31 days
plot_acf(df.values.squeeze(), lags=31)
plt.show() | _____no_output_____ | CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
1.2.2 Partial autocorrelation | # Plot partial autocorrelation for lags between 1 and 730 days
plot_pacf(df.values.squeeze(), lags=730)
plt.show()
# Plot partial autocorrelation for lags between 1 and 31 days
plot_pacf(df.values, lags=31)
plt.show() | _____no_output_____ | CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
The correlation seem to be the strongest with a lag which is a multiple of 180.2 (1/2 year), but verry bad monthly or with any other lag. 2. Modeling 2.0 Modeling: AR from scratch (just as an example, nothing to do here)AR stands for AutoRegressive. Autoregressive models describe the value of any points in a timeseri... | # We store all values of the series in a numpy array called series
series = df["Temp"].values
def auto_regression(series, order):
n_points = len(series)
# All lagged values will be stored in y_lag.
# If order is 7, for each timestep we will store 7 values.
X_lag = np.zeros((order, n_points-order))... | _____no_output_____ | CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
Now that we have our coefficients learned, we can make predictions. | lag = beta.shape[1]
Y_truth = [] # real timeseries
Y_pred = [] # predictions
for i in range(0, len(series)-lag-1):
# apply the equation of AR using the coefficients at each time steps
y = alpha + np.dot(beta, series[i:i+lag]) # y[t] = alpha + y[t-1]*beta1 + y[t-2]*beta2 + ...
Y_pred.append(y)
Y_tru... | _____no_output_____ | CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
And here are our coefficients: | coefs = np.c_[alpha, beta]
plt.bar(np.arange(coefs.shape[1]), coefs.flatten())
labels = ['$\\alpha$']
for i in range(beta.shape[1]):
labels.append(f"$\\beta_{i+1}$")
plt.xticks(np.arange(coefs.shape[1]), labels)
plt.show() | _____no_output_____ | CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
2.1 Modeling : ARIMA | from statsmodels.tsa.arima.model import ARIMA | _____no_output_____ | CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
ARIMA is an acronym that stands for AutoRegressive Integrated Moving Average, capturing the key aspects of the model :- **AR** : *AutoRegressive* A model that uses the dependent relationship between an observation and some number of lagged observations.A pure AR model is such that :$$Y_t = \alpha + \beta_1 Y_{t-1} + \b... | # seasonal difference
differenced = df.diff(365)
# trim off the first year of empty data
differenced = differenced[365:]
# Create an ARIMA model (check the statsmodels docs) (p,d,q)
# d = 0 because the serie is stationary (see before)
model = ARIMA(series, order=(3, 0, 15))
# fit model
model_fit = model.fit()
print(m... | _____no_output_____ | CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
To evaluate the ARIMA model, we will use walk forward validation. First we split the data into a training and testing set (initially, a year is a good interval to test for this dataset given the seasonal nature). A model will be constructed on the historic data and predict the next time step. The real observation of th... | from math import sqrt
from sklearn.metrics import mean_squared_error
# rolling forecast with ARIMA
train, test = differenced.iloc[:-365], differenced.iloc[-365:]
# walk-forward validation
values = train.values
history = [v for v in values]
predictions = list()
test_values = test.values
for t in range(len(test_values... | RSME : 3.0962731960398195
| CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
We can also use the predict() function on the results object to make predictions. It accepts the index of the time steps to make predictions as arguments. These indexes are relative to the start of the training dataset. | forecast = model_fit.predict(start=len(train.values), end=len(differenced.values), typ='levels')
plt.plot(test)
plt.plot(forecast, color='red')
plt.show() | _____no_output_____ | CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
Exercise: Mauna Loa CO2 concentration levels (1975 - 2021)Carbon dioxyde (CO2) is a gas naturaly present in our environment. However, the concentration of CO2 is increasing every year, mainly because of human activities. It is one of the major cause of global warming, and its value is precautiounously measured since 1... | ts = pd.read_csv("https://gml.noaa.gov/aftp/data/trace_gases/co2/in-situ/surface/mlo/co2_mlo_surface-insitu_1_ccgg_MonthlyData.txt",
header=150, sep=" ")
ts = ts[ts["year"] > 1975]
time_index = pd.DatetimeIndex(pd.to_datetime(ts[["year", "month", "day"]]))
ts = ts.set_index(time_index)
ts = pd.Series... | p-value (95% confidence interval): 5.4641e-36, statistics: 162.39
| CC0-1.0 | TD1_Timeseries_Analysis.ipynb | RomainBnfn/Timeseries-Sequence-Processing-2021 |
🍔 K Means Clustering Step By Step Installation | # remove `!` if running the line in a terminal
!pip install -U RelevanceAI[notebook]==2.0.0 | _____no_output_____ | Apache-2.0 | guides/kmeans_clustering_step_by_step_guide.ipynb | RelevanceAI/RelevanceAI |
Setup First, you need to set up a client object to interact with RelevanceAI. | from relevanceai import Client
client = Client() | _____no_output_____ | Apache-2.0 | guides/kmeans_clustering_step_by_step_guide.ipynb | RelevanceAI/RelevanceAI |
Data You will need to have a dataset under your Relevance AI account. You can either use our e-commerce dataset as shown below or follow the tutorial on how to create your own dataset.Our e-commerce dataset includes fields such as `product_title`, as well as the vectorized version of the field `product_title_clip_vect... | from relevanceai.utils.datasets import get_ecommerce_dataset_encoded
documents = get_ecommerce_dataset_encoded()
{k: v for k, v in documents[0].items() if "_vector_" not in k} | _____no_output_____ | Apache-2.0 | guides/kmeans_clustering_step_by_step_guide.ipynb | RelevanceAI/RelevanceAI |
Upload the data to Relevance AI Run the following cell, to upload these documents into your personal Relevance AI account under the name `quickstart_clustering_kmeans` | ds = client.Dataset("quickstart_kmeans_clustering")
ds.insert_documents(documents) | _____no_output_____ | Apache-2.0 | guides/kmeans_clustering_step_by_step_guide.ipynb | RelevanceAI/RelevanceAI |
Check the data | ds.health()
ds.schema | _____no_output_____ | Apache-2.0 | guides/kmeans_clustering_step_by_step_guide.ipynb | RelevanceAI/RelevanceAI |
Clustering We apply the Kmeams clustering algorithm to the vector field, `product_title_clip_vector_` | from sklearn.cluster import KMeans
VECTOR_FIELD = "product_title_clip_vector_"
KMEAN_NUMBER_OF_CLUSTERS = 5
ALIAS = "kmeans_" + str(KMEAN_NUMBER_OF_CLUSTERS)
model = KMeans(n_clusters=KMEAN_NUMBER_OF_CLUSTERS)
clusterer = client.ClusterOps(alias=ALIAS, model=model)
clusterer.run(
dataset_id="quickstart_kmeans_clu... | _____no_output_____ | Apache-2.0 | guides/kmeans_clustering_step_by_step_guide.ipynb | RelevanceAI/RelevanceAI |
We download a small sample and show the clustering results using our json_shower. | from relevanceai import show_json
sample_documents = ds.sample(n=5)
samples = [
{
"product_title": d["product_title"],
"cluster": d["_cluster_"][VECTOR_FIELD][ALIAS],
}
for d in sample_documents
]
show_json(samples, text_fields=["product_title", "cluster"]) | _____no_output_____ | Apache-2.0 | guides/kmeans_clustering_step_by_step_guide.ipynb | RelevanceAI/RelevanceAI |
Image Manipulation with skimage This example builds a simple UI for performing basic image manipulation with [scikit-image](http://scikit-image.org/). | # Stdlib imports
from io import BytesIO
# Third-party libraries
from IPython.display import Image
from ipywidgets import interact, interactive, fixed
import matplotlib as mpl
from skimage import data, filters, io, img_as_float
import numpy as np | _____no_output_____ | BSD-3-Clause | docs/source/examples/Image Processing.ipynb | akhand1111/ipywidgets |
Let's load an image from scikit-image's collection, stored in the `data` module. These come back as regular numpy arrays: | i = img_as_float(data.coffee())
i.shape | _____no_output_____ | BSD-3-Clause | docs/source/examples/Image Processing.ipynb | akhand1111/ipywidgets |
Let's make a little utility function for displaying Numpy arrays with the IPython display protocol: | def arr2img(arr):
"""Display a 2- or 3-d numpy array as an image."""
if arr.ndim == 2:
format, cmap = 'png', mpl.cm.gray
elif arr.ndim == 3:
format, cmap = 'jpg', None
else:
raise ValueError("Only 2- or 3-d arrays can be displayed as images.")
# Don't let matplotlib autoscale... | _____no_output_____ | BSD-3-Clause | docs/source/examples/Image Processing.ipynb | akhand1111/ipywidgets |
Now, let's create a simple "image editor" function, that allows us to blur the image or change its color balance: | def edit_image(image, sigma=0.1, R=1.0, G=1.0, B=1.0):
new_image = filters.gaussian(image, sigma=sigma, multichannel=True)
new_image[:,:,0] = R*new_image[:,:,0]
new_image[:,:,1] = G*new_image[:,:,1]
new_image[:,:,2] = B*new_image[:,:,2]
return arr2img(new_image) | _____no_output_____ | BSD-3-Clause | docs/source/examples/Image Processing.ipynb | akhand1111/ipywidgets |
We can call this function manually and get a new image. For example, let's do a little blurring and remove all the red from the image: | edit_image(i, sigma=5, R=0.1) | _____no_output_____ | BSD-3-Clause | docs/source/examples/Image Processing.ipynb | akhand1111/ipywidgets |
But it's a lot easier to explore what this function does by controlling each parameter interactively and getting immediate visual feedback. IPython's `ipywidgets` package lets us do that with a minimal amount of code: | lims = (0.0,1.0,0.01)
interact(edit_image, image=fixed(i), sigma=(0.0,10.0,0.1), R=lims, G=lims, B=lims); | _____no_output_____ | BSD-3-Clause | docs/source/examples/Image Processing.ipynb | akhand1111/ipywidgets |
Browsing the scikit-image gallery, and editing grayscale and jpg imagesThe coffee cup isn't the only image that ships with scikit-image, the `data` module has others. Let's make a quick interactive explorer for this: | def choose_img(name):
# Let's store the result in the global `img` that we can then use in our image editor below
global img
img = getattr(data, name)()
return arr2img(img)
# Skip 'load' and 'lena', two functions that don't actually return images
interact(choose_img, name=sorted(set(data.__all__)-{'len... | _____no_output_____ | BSD-3-Clause | docs/source/examples/Image Processing.ipynb | akhand1111/ipywidgets |
And now, let's update our editor to cope correctly with grayscale and color images, since some images in the scikit-image collection are grayscale. For these, we ignore the red (R) and blue (B) channels, and treat 'G' as 'Grayscale': | lims = (0.0, 1.0, 0.01)
def edit_image(image, sigma, R, G, B):
new_image = filters.gaussian(image, sigma=sigma, multichannel=True)
if new_image.ndim == 3:
new_image[:,:,0] = R*new_image[:,:,0]
new_image[:,:,1] = G*new_image[:,:,1]
new_image[:,:,2] = B*new_image[:,:,2]
else:
... | _____no_output_____ | BSD-3-Clause | docs/source/examples/Image Processing.ipynb | akhand1111/ipywidgets |
Compare phase estimation methods on hippocampal theta oscillations | import numpy as np
import scipy as sp
%matplotlib notebook
%config InlineBackend.figure_format = 'retina'
%matplotlib inline
import matplotlib.pyplot as plt | _____no_output_____ | MIT | misc/.ipynb_checkpoints/Theta phase estimate-checkpoint.ipynb | srcole/qwm |
Load data | x = np.load('/gh/data2/hc3/npy/gor01/2006-6-7_16-40-19/10/lfp_0.npy')
x = x[10000:30000]
Fs = 1252 | _____no_output_____ | MIT | misc/.ipynb_checkpoints/Theta phase estimate-checkpoint.ipynb | srcole/qwm |
Preprocess data | cflow = 50
Ntapslow = 501
cfhigh = 1
Ntapshigh = 1001
from misshapen import nonshape
x = nonshape.highpass_default(x, Fs, cfhigh, Ntapshigh)
x = nonshape.lowpass_default(x, Fs, cflow, Ntapslow) | _____no_output_____ | MIT | misc/.ipynb_checkpoints/Theta phase estimate-checkpoint.ipynb | srcole/qwm |
Compute phase time series Bandpass + hilbert | f_range = (4,10)
x_filt, _ = nonshape.bandpass_default(x, f_range, Fs, rmv_edge=False)
pha_h = np.angle(sp.signal.hilbert(x_filt)) | _____no_output_____ | MIT | misc/.ipynb_checkpoints/Theta phase estimate-checkpoint.ipynb | srcole/qwm |
Peak and trough interpolation | Ps, Ts = nonshape.findpt(x, f_range, Fs)
pha_pt = nonshape.wfpha(x, Ps, Ts) | _____no_output_____ | MIT | misc/.ipynb_checkpoints/Theta phase estimate-checkpoint.ipynb | srcole/qwm |
Compare phase time series | t = np.arange(0,len(x)/Fs, 1/Fs)
tlims = [1,3]
samps = np.logical_and(t>=tlims[0],t<tlims[1])
plt.figure(figsize=(16,6))
plt.subplot(2,1,1)
plt.plot(t[samps],x[samps],'k')
plt.xlim(tlims)
plt.ylabel('Voltage (uV)',size=20)
plt.subplot(2,1,2)
plt.plot(t[samps],pha_h[samps],'r',label='Hilbert')
plt.plot(t[samps],pha_pt[... | _____no_output_____ | MIT | misc/.ipynb_checkpoints/Theta phase estimate-checkpoint.ipynb | srcole/qwm |
Downloading data dynamically | # Required libraries
import os
import tarfile
import urllib
# DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml2/master/"
DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml/master/"
# HOUSING_PATH = os.path.join('datasets', 'housing')
HOUSING_PATH = '../Datasets'
HOUSING_URL = DOWNL... | 1
2
3
*File downloaded and extracted successfully*
| MIT | Projects/Housing/Code/Housing data download.ipynb | drnesr/Machine_Learning |
Load dataset:MNIST and CIFAR10 | mnist = fetch_openml("mnist_784")
mnist.data.shape
print('Data: {}, target: {}'.format(mnist.data.shape, mnist.target.shape))
X_train, X_test, y_train, y_test = train_test_split(
mnist.data,
mnist.target,
test_size=1/7,
random_state=0,
)
X_train = X_train.values.reshape((len(X_train), 784))
X_test = X... | _____no_output_____ | MIT | PSForest_example.ipynb | nishiwen1214/PSForest |
Using the PSForest | start =time.clock()
before_mem = memory_profiler.memory_usage()
# Create PSForest model
ps_forest = PSForest(
estimators_config={
'mgs': [{
'estimator_class': RandomForestClassifier,
'estimator_params': {
'n_estimators': 500,
'max_features': 1,
... | accuracy: 0.896
| MIT | PSForest_example.ipynb | nishiwen1214/PSForest |
Optional side-effect | val nameOpt = Option("Amir")
def printName(name: String) = println(name)
nameOpt.foreach(printName)
val anotherOpt = None
anotherOpt.foreach(printName) | _____no_output_____ | MIT | scala/Optional side-effect.ipynb | ashishpatel26/learning |
Agenda- Why Logging- How does Logging work for you?- Optional Content The Presentation- The slides, support code and jypyter notebook are on Github- [https://github.com/stbaercom/europython2015_logging](https://github.com/stbaercom/europython2015_logging) A Simple Program, Without any Logging | from datetime import datetime
def my_division_p(dividend, divisor):
try:
print("Debug, Division : {}/{}".format(dividend,divisor))
result = dividend / divisor
return result
except (ZeroDivisionError, TypeError):
print("Error, Division Failed")
return None
def division_ta... | _____no_output_____ | MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
Let us Have a Look at the Output | task = [(3,4),(5,1.4),(2,0),(3,5),("10",1)]
division_task_handler_p(task) | Handling division task,5 items
Doing devision iteration 0 on 2015
Debug, Division : 3/4
Doing devision iteration 1 on 2015
Debug, Division : 5/1.4
Doing devision iteration 2 on 2015
Debug, Division : 2/0
Error, Division Failed
Doing devision iteration 3 on 2015
Debug, Division : 3/5
Doing devision iteration 4 on 2015
D... | MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
The Problems with ``print()``- We don't have a way to select the types of messages we are interested in- We have to add all information (timestamps, etc...) by ourselves- All our messages will look slightly different- We have only limited control where our message end up What is Different with Logging?- We have more... | import log1; logging = log1.get_clean_logging()
logging.basicConfig(level=logging.DEBUG)
log = logging.getLogger()
def my_division(dividend, divisor):
try:
log.debug("Division : %s/%s", dividend, divisor)
result = dividend / divisor
return result
except (ZeroDivisionError, TypeError):
... | _____no_output_____ | MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
The Call and the Log Messages | task = [(3,4),(2,0),(3,5),("10",1)]
division_task_handler(task) | INFO:root:Handling division task,4 items
INFO:root:Doing devision iteration 0
DEBUG:root:Division : 3/4
INFO:root:Doing devision iteration 1
DEBUG:root:Division : 2/0
ERROR:root:Error, Division Failed
Traceback (most recent call last):
File "<ipython-input-10-a904db1e3e23>", line 8, in my_division
result = divide... | MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
How does the Logging Module represent these Aspect Back to Code. How does Logging Work? | import log1;logging = log1.get_clean_logging() # this would be import logging outside this notebook
logging.debug("Find me in the log")
logging.info("I am hidden")
logging.warn("I am here")
logging.error("As am I")
try:
1/0;
except:
logging.exception(" And I")
logging.critical("Me, of course") | WARNING:root:I am here
ERROR:root:As am I
ERROR:root: And I
Traceback (most recent call last):
File "<ipython-input-12-75f8227eec02>", line 8, in <module>
1/0;
ZeroDivisionError: division by zero
CRITICAL:root:Me, of course
| MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
More Complex Logging Setup with ``basicConfig()`` | import log1;logging = log1.get_clean_logging()
datefmt = "%Y-%m-%d %H:%M:%S"
msgfmt = "%(asctime)s,%(msecs)03d %(levelname)-10s %(name)-15s : %(message)s"
logging.basicConfig(level=logging.DEBUG, format=msgfmt, datefmt=datefmt)
logging.debug("Now I show up ")
logging.info("Now this is %s logging!","good")
logging.warn... | 2015-07-19 20:19:55,551 DEBUG root : Now I show up
2015-07-19 20:19:55,552 INFO root : Now this is good logging!
2015-07-19 20:19:55,552 WARNING root : I am here. 1 + 3 = 4
2015-07-19 20:19:55,552 ERROR root : As am I
2015-07-19 20:19:55,553 ERROR ... | MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
Some (personal) Remarks about ``basicConfig()``- `basicConfig()` does save you some typing, but I would go for the 'normal' setup. - Using `basicConfig()` is a matter of personal taste.- The normal setup makes the structure clearer.- Keep in mind that basicConfig() is meant to be called once... Using the Standard Co... | import log1, json, logging.config;logging = log1.get_clean_logging()
datefmt = "%Y-%m-%d %H:%M:%S"
msgfmt = "%(asctime)s,%(msecs)03d %(levelname)-6s %(name)-10s : %(message)s"
log = logging.getLogger()
log.setLevel(logging.DEBUG)
lh = logging.StreamHandler()
lf = logging.Formatter(fmt=msgfmt, datefmt=datefmt)
lh.se... | 2015-07-19 20:19:55,571 INFO root : Now this is good logging!
2015-07-19 20:19:55,572 DEBUG root : A slightly more complex message 1 + 2 = 3
| MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
Now, back to the Theory. What have we Build? How do we get from the Configuration to the Log Message? Formatting : Attributes Available for the Logging Call AttributeDescriptionargsTuple of arguments passed to the logging callasctimeLog record creation time, formattedcreatedLog record creation time, seconds since t... | import log1, json, logging.config;logging = log1.get_clean_logging()
conf_dict = {
'version': 1,
'disable_existing_loggers': True,
'formatters': {
'longformat': {
'format': "%(asctime)s,%(msecs)03d %(levelname)-10s %(name)-15s : %(message)s",
'datefmt': "%Y-%m-%d %H:%M:%S"}}... | 2015-07-19 20:19:55,602 INFO root : Now this is good logging!
| MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
Adding a ``Filehandler`` to the Logger | import log1, json, logging.config;logging = log1.get_clean_logging()
base_config = json.load(open("conf_dict.json"))
base_config['handlers']['logfile'] = {
'class' : 'logging.FileHandler',
'mode' : 'w',
'filename' : 'logfile.txt',
'formatter': "longformat"}
base_config['loggers']['']['handlers'].appen... | 2015-07-19 20:19:55,618 INFO root : Now this is good logging!
| MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
Another look at the logging object tree Set the Level on the ``FileHandler`` | import log1, json, logging.config;logging = log1.get_clean_logging()
file_config = json.load(open("conf_dict_with_file.json"))
file_config['handlers']['logfile']['level'] = "WARN"
logging.config.dictConfig(file_config)
log = logging.getLogger()
log.info("Now this is %s logging!","good")
log.warning("Now this is %s l... | 2015-07-20 19:04:03,132 INFO root : Now this is good logging!
2015-07-20 19:04:03,133 WARNING root : Now this is worrisome logging!
| MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
Adding Child Loggers under the Root | import log1,json,logging.config;logging = log1.get_clean_logging()
logging.config.dictConfig(json.load(open("conf_dict.json")))
log = logging.getLogger("")
child_A = logging.getLogger("A")
child_B = logging.getLogger("B")
child_B_A = logging.getLogger("B.A")
log.info("Now this is %s logging!","good")
child_A.info("... | 2015-07-19 20:19:55,865 INFO root : Now this is good logging!
2015-07-19 20:19:55,866 INFO A : Now this is more logging!
2015-07-19 20:19:55,867 WARNING root : Now this is worrisome logging!
| MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
Looking at the tree of Logging Objects Best Practices for the Logging Tree- Use ``.getLogger(__name__)`` per module to define loggers under the root logger- Set propagate to True on each Logger- Attach Handlers and Filters as needed to control output from the Logging hierarchy Filter - Now that things are Getting C... | import log1,json,logging.config;logging = log1.get_clean_logging()
logging.config.dictConfig(json.load(open("conf_dict.json")))
def log_filter(rec): # Callables work with 3.2 and later
if 'please' in rec.msg.lower():
return True
return False
log = logging.getLogger("")
log.addFilter(log_filter)
child... | 2015-07-20 08:01:55,108 INFO A : Just log me
2015-07-20 08:01:55,108 INFO root : Hallo, Please log me
| MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
The Way of a Logging Record A second Example for Filters, in the LogHandler | import log1, json, logging.config;logging = log1.get_clean_logging()
datefmt = "%Y-%m-%d %H:%M:%S"
msgfmt = "%(asctime)s,%(msecs)03d %(levelname)-6s %(name)-10s : %(message)s"
log_reg = None
def handler_filter(rec): # Callables work with 3.2 and later
global log_reg
if 'please' in rec.msg.lower():
rec.... | 2015-07-19 20:19:55,905 WARNING root : Hi, I am LOGGY. I am 11 seconds old. Please log me (I am nice)
| MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
Things you might want to know ( if we still have some time) A short look at our LogRecord | print(log_reg)
log_reg.__dict__ | <LogRecord: root, 30, <ipython-input-20-d1d101ab918f>, 25, "Hi, I am %s. I am %i seconds old. Please log me (I am nice)">
| MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
Logging Performance - Slow, but Fast EnoughScenario (10000 Call, 3 Logs per call)RuntimeFull Logging with buffered writes3.096sDisable Caller information2.868sCheck Logging Lvl before Call, Logging disabled0.186sLogging module level disabled0.181sNo Logging calls at all0.157s Getting the current Logging Tree | import json, logging.config
config = json.load(open("conf_dict_with_file.json"))
logging.config.dictConfig(config)
import requests
import logging_tree
logging_tree.printout() | <--""
Level DEBUG
Handler Stream <IPython.kernel.zmq.iostream.OutStream object at 0x105d043c8>
Formatter fmt='%(asctime)s,%(msecs)03d %(levelname)-10s %(name)-15s : %(message)s' datefmt='%Y-%m-%d %H:%M:%S'
Handler File '/Users/imhiro/AllFiles/0021_travel_events_conferences_workshops/2015-07-19_europython/... | MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
Reconfiguration- It is possible to change the logging configuration at runtime- It is even part of the standard library- Still, some caution is in order Reloading the configuration _can_ disable the existing loggers | import log1,json,logging,logging.config;logging = log1.get_clean_logging()
#Load Config, define a child logger (could also be a module)
logging.config.dictConfig(json.load(open("conf_dict_with_file.json")))
child_log = logging.getLogger("somewhere")
#Reload Config
logging.config.dictConfig(json.load(open("conf_dict... | _____no_output_____ | MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
Reloading can happen in place | import log1, json, logging, logging.config;logging = log1.get_clean_logging()
config = json.load(open("conf_dict_with_file.json"))
#Load Config, define a child logger (could also be a module)
logging.config.dictConfig(config)
child_log = logging.getLogger("somewhere")
config['disable_existing_loggers'] = False
#Rel... | 2015-07-19 20:20:42,290 INFO somewhere : Now this is good logging!
| MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
Successful Logging to all of You | from presentation_helper import customize_settings
customize_settings() | _____no_output_____ | MIT | europython_2015_logging_talk.ipynb | stbaercom/europython2015_logging |
%%capture
pip install arviz
import numpy as np
import pymc3 as pm
import theano.tensor as tt
import arviz as az
import matplotlib.pyplot as plt
import seaborn as sns | _____no_output_____ | MIT | ThinkBayes_Chapter_9.ipynb | ricardoV94/ThinkBayesPymc3 | |
9.1 Paintball | def StrafingSpeed(alpha, beta, x):
theta = tt.arctan2(x-alpha, beta)
speed = beta / tt.cos(theta)**2
return speed
with pm.Model() as m_9_3:
obs_beta = pm.Data('obs_beta', [10])
alpha = pm.Uniform('alpha', lower=0, upper=31, observed=10)
beta = pm.Uniform('beta', lower=1, upper=51, observed=obs... | _____no_output_____ | MIT | ThinkBayes_Chapter_9.ipynb | ricardoV94/ThinkBayesPymc3 |
9.5 Joint distributionsResults are very different from those of the book. Posterior is much more narrow. | with pm.Model() as m_9_5:
alpha = pm.Uniform('alpha', lower=0, upper=31)
beta = pm.Uniform('beta', lower=1, upper=51)
location = pm.Uniform('location', lower=0, upper=31, observed=[15, 16, 18, 21])
speed = pm.Deterministic('speed', StrafingSpeed(alpha, beta, location))
like = pm.Potential('like', ... | _____no_output_____ | MIT | ThinkBayes_Chapter_9.ipynb | ricardoV94/ThinkBayesPymc3 |
9.6 Conditional Distributions | with pm.Model() as m_9_6:
obs_beta = pm.Data('obs_beta', 10)
alpha = pm.Uniform('alpha', lower=0, upper=31)
beta = pm.Uniform('beta', lower=1, upper=51, observed=obs_beta)
location = pm.Uniform('location', lower=0, upper=31, observed=[15, 16, 18, 21])
speed = pm.Deterministic('speed', StrafingSpe... | _____no_output_____ | MIT | ThinkBayes_Chapter_9.ipynb | ricardoV94/ThinkBayesPymc3 |
Multiple Linear Regression | import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
diabetes = datasets.load_diabetes() #load dataset
diabetes_X = diabetes.data
X_train, X_test, y_train, y_test = tra... | Coefficients:
[[ 37.90031426 -241.96624835 542.42575342 347.70830529 -931.46126093
518.04405547 163.40353476 275.31003837 736.18909839 48.67112488]]
Mean squared error: 2900.17
Variance score: 0.45
| MIT | Module 4/multilinear_regression.ipynb | axel-sirota/interpreting-data-with-advanced-models |
Handling missing values | # get the number of missing data points per column
missing_values_count = nfl_data.isnull().sum()
# how many total missing values do we have?
total_cells = np.product(nfl_data.shape)
total_missing = missing_values_count.sum()
# percent of data that is missing
percent_missing = (total_missing/total_cells) * 100
print(... | _____no_output_____ | MIT | Data_Cleaning.ipynb | duartele/exerc-jupyternotebook |
Scaling and normalization | # for Box-Cox Transformation
from scipy import stats
# for min_max scaling
from mlxtend.preprocessing import minmax_scaling
# set seed for reproducibility
np.random.seed(0) | _____no_output_____ | MIT | Data_Cleaning.ipynb | duartele/exerc-jupyternotebook |
In scaling, you're changing the range of your data | # generate 1000 data points randomly drawn from an exponential distribution
original_data = np.random.exponential(size=1000)
# mix-max scale the data between 0 and 1
scaled_data = minmax_scaling(original_data, columns=[0])
# plot both together to compare
fig, ax = plt.subplots(1,2)
sns.distplot(original_data, ax=ax[0... | _____no_output_____ | MIT | Data_Cleaning.ipynb | duartele/exerc-jupyternotebook |
In normalization, you're changing the shape of the distribution of your data. | # normalize the exponential data with boxcox
normalized_data = stats.boxcox(original_data) | _____no_output_____ | MIT | Data_Cleaning.ipynb | duartele/exerc-jupyternotebook |
Parsing Dates - it will be "object" if you don't parse it. | import datetime | _____no_output_____ | MIT | Data_Cleaning.ipynb | duartele/exerc-jupyternotebook |
if a date is MM/DD/YY (02/25/17) the format is "%m/%d/%y""%Y" (upper y) is used if the year has four digits (2017)That is (02/25/2017) the format is "%m/%d/%Y"Other formats: DD/MM/YY (25/02/17 - "%d/%m/%y") ; DD-MM-YY (25-02-17 - "%d-%m-%y"). At the end, the date will be show as the defaut YYYY-MM-DD (datetime64) | # create a new column, date_parsed, with the parsed dates
landslides['date_parsed'] = pd.to_datetime(landslides['date'], format="%m/%d/%y") | _____no_output_____ | MIT | Data_Cleaning.ipynb | duartele/exerc-jupyternotebook |
If your dates is with multiple formats, you can use "infer_datetime_format". | landslides['date_parsed'] = pd.to_datetime(landslides['Date'], infer_datetime_format=True)
day_of_month_landslides = landslides['date_parsed'].dt.day | _____no_output_____ | MIT | Data_Cleaning.ipynb | duartele/exerc-jupyternotebook |
To check if everything looks right | date_lengths = earthquakes.Date.str.len()
date_lengths.value_counts()
#or showing trhough a histogram of the day (values must be in [0,31] )
#In that example, 3 dates have len of 24. So we run this code
indices = np.where([date_lengths == 24])[1]
print('Indices with corrupted data:', indices)
earthquakes.loc[indices]
... | _____no_output_____ | MIT | Data_Cleaning.ipynb | duartele/exerc-jupyternotebook |
Character Encodings | # helpful character encoding module
import chardet
# look at the first ten thousand bytes to guess the character encoding
with open("../input/kickstarter-projects/ks-projects-201801.csv", 'rb') as rawdata:
result = chardet.detect(rawdata.read(10000))
# check what the character encoding might be
print(result)
# rea... | _____no_output_____ | MIT | Data_Cleaning.ipynb | duartele/exerc-jupyternotebook |
Exercises | #1 - class bytes We need to create a new_entry in bytes (UTF-8is defaut)
sample_entry = b'\xa7A\xa6n'
print(sample_entry)
print('data type:', type(sample_entry))
#solution - Try using .decode() to get the string, then .encode() to get the bytes representation, encoded in UTF-8.
before = sample_entry.decode("big5-tw")
n... | _____no_output_____ | MIT | Data_Cleaning.ipynb | duartele/exerc-jupyternotebook |
Inconsistent Data | # helpful modules
import fuzzywuzzy
from fuzzywuzzy import process
import chardet
# get the top 10 closest matches to "south korea"
# The closer str has ratio of 100
matches = fuzzywuzzy.process.extract("south korea", countries, limit=10, scorer=fuzzywuzzy.fuzz.token_sort_ratio)
# function to replace rows in the provid... | _____no_output_____ | MIT | Data_Cleaning.ipynb | duartele/exerc-jupyternotebook |
Scraping Fantasy Football Data (Week 3 Projections/Week 2 Actuals)Need to scrape the following data:- Weekly Player PPR Projections: ESPN, CBS, Fantasy Sharks, Scout Fantasy Sporsts, (and tried Fantasy Football Today but doesn't have defense projections currently, so exclude)- Previous Week Player Actual PPR Results-... | import pandas as pd
import numpy as np
import requests
# import json
# from bs4 import BeautifulSoup
import time
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
# from se... | _____no_output_____ | MIT | data/Scraping Fantasy Football Data - FINAL-Week3.ipynb | zgscherrer/Project-Fantasy-Football |
Get Weekly Player Actual Fantasy PPR PointsGet from ESPN's Scoring Leaders tablehttp://games.espn.com/ffl/leaders?&scoringPeriodId=1&seasonId=2018&slotCategoryId=0&leagueID=0- scoringPeriodId = week of the season- seasonId = year- slotCategoryId = position, where 'QB':0, 'RB':2, 'WR':4, 'TE':6, 'K':17, 'D/ST':16- leag... | ##SCRAPE ESPN SCORING LEADERS TABLE FOR ACTUAL FANTASY PPR POINTS##
#input needs to be year as four digit number and week as number
#returns dataframe of scraped data
def scrape_actual_PPR_player_points_ESPN(week, year):
#instantiate the driver
driver = instantiate_selenium_driver()
#initialize dataf... | Pickle saved to: pickle_archive/Week2_Player_Actual_PPR_messy_scrape_2018-9-18-17-59.pkl
Pickle saved to: pickle_archive/Week2_Player_Actual_PPR_2018-9-18-17-59.pkl
(1009, 5)
| MIT | data/Scraping Fantasy Football Data - FINAL-Week3.ipynb | zgscherrer/Project-Fantasy-Football |
Get ESPN Player Fantasy Points Projections for Week Get from ESPN's Projections Tablehttp://games.espn.com/ffl/tools/projections?&scoringPeriodId=1&seasonId=2018&slotCategoryId=0&leagueID=0- scoringPeriodId = week of the season- seasonId = year- slotCategoryId = position, where 'QB':0, 'RB':2, 'WR':4, 'TE':6, 'K':17, ... | ##SCRAPE ESPN PROJECTIONS TABLE FOR PROJECTED FANTASY PPR POINTS##
#input needs to be year as four digit number and week as number
#returns dataframe of scraped data
def scrape_weekly_player_projections_ESPN(week, year):
#instantiate the driver on the ESPN projections page
driver = instantiate_selenium_driver... | Pickle saved to: pickle_archive/Week2_PPR_Projections_ESPN_messy_scrape_2018-9-18-18-3.pkl
Pickle saved to: pickle_archive/Week3_PPR_Projections_ESPN_2018-9-18-18-3.pkl
(1009, 5)
| MIT | data/Scraping Fantasy Football Data - FINAL-Week3.ipynb | zgscherrer/Project-Fantasy-Football |
Get CBS Player Fantasy Points Projections for Week Get from CBS's Projections Tablehttps://www.cbssports.com/fantasy/football/stats/sortable/points/QB/ppr/projections/2018/2?&print_rows=9999- QB is where position goes- 2018 is where season goes- 2 is where week goes- print_rows = 9999 gives all results in one table | ##SCRAPE CBS PROJECTIONS TABLE FOR PROJECTED FANTASY PPR POINTS##
#input needs to be year as four digit number and week as number
#returns dataframe of scraped data
def scrape_weekly_player_projections_CBS(week, year):
###GET PROJECTIONS FROM CBS###
#CBS has separate tables for each position, so need to cycle... | Pickle saved to: pickle_archive/Week3_PPR_Projections_CBS_messy_scrape_2018-9-18-18-4.pkl
Pickle saved to: pickle_archive/Week3_PPR_Projections_CBS_2018-9-18-18-4.pkl
(830, 5)
| MIT | data/Scraping Fantasy Football Data - FINAL-Week3.ipynb | zgscherrer/Project-Fantasy-Football |
Get Fantasy Sharks Player Points Projection for WeekThey have a json option that gets updated weekly (don't appear to store previous week projections). The json defaults to PPR (which is lucky for us) and has an all players option.https://www.fantasysharks.com/apps/Projections/WeeklyProjections.php?pos=ALL&format=jso... | ##SCRAPE FANTASY SHARKS PROJECTIONS TABLE FOR PROJECTED FANTASY PPR POINTS##
#input needs to be week as number (year isn't used, but keep same format as others)
#returns dataframe of scraped data
def scrape_weekly_player_projections_Sharks(week, year):
#fantasy sharks url - segment for 2018 week 1 starts at 628 an... | Pickle saved to: pickle_archive/Week3_PPR_Projections_Sharks_messy_scrape_2018-9-18-18-4.pkl
Pickle saved to: pickle_archive/Week3_PPR_Projections_Sharks_2018-9-18-18-4.pkl
(971, 5)
| MIT | data/Scraping Fantasy Football Data - FINAL-Week3.ipynb | zgscherrer/Project-Fantasy-Football |
Get Scout Fantasy Sports Player Fantasy Points Projections for Week Get from Scout Fantasy Sports Projections Tablehttps://fftoolbox.scoutfantasysports.com/football/rankings/?pos=rb&week=2&noppr=false- pos is position with options of 'QB','RB','WR','TE', 'K', 'DEF'- week is week of year- noppr is set to false when you... | ##SCRAPE Scout PROJECTIONS TABLE FOR PROJECTED FANTASY PPR POINTS##
#input needs to be year as four digit number and week as number
#returns dataframe of scraped data
def scrape_weekly_player_projections_SCOUT(week, year):
###GET PROJECTIONS FROM SCOUT###
#SCOUT has separate tables for each position, so need ... | C:\Users\micha\Anaconda3\envs\PythonData\lib\site-packages\urllib3\connectionpool.py:858: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings
InsecureRequestWarning)
C:\Users... | MIT | data/Scraping Fantasy Football Data - FINAL-Week3.ipynb | zgscherrer/Project-Fantasy-Football |
Get FanDuel Player Salaries for Week just import the Thurs-Mon game salaries (they differ for each game type, and note they don't include Kickers in the Thurs-Mon)Go to a FanDuel Thurs-Mon competition and Download a csv of players, which we then upload and format in python. | ###FORMAT/EXTRACT FANDUEL SALARY INFO###
def format_extract_FanDuel(df_fanduel_csv, week, year):
#rename columns
df_fanduel_csv.rename(columns={'Position':'POS',
'Nickname':'PLAYER',
'Team':'TEAM',
'Sala... | Pickle saved to: pickle_archive/Week3_Salary_FanDuel_2018-9-18-18-5.pkl
(665, 5)
| MIT | data/Scraping Fantasy Football Data - FINAL-Week3.ipynb | zgscherrer/Project-Fantasy-Football |
!!!FFtoday apparently doesn't do weekly projections for Defenses, so don't use it for now (can check back in future and see if updated)!!! Get FFtoday Player Fantasy Points Projections for Week Get from FFtoday's Projections Tablehttp://www.fftoday.com/rankings/playerwkproj.php?Season=2018&GameWeek=2&PosID=10&LeagueID... | # ##SCRAPE FFtoday PROJECTIONS TABLE FOR PROJECTED FANTASY PPR POINTS##
# #input needs to be year as four digit number and week as number
# #returns dataframe of scraped data
# def scrape_weekly_player_projections_FFtoday(week, year):
# #instantiate selenium driver
# driver = instantiate_selenium_driver()
... | _____no_output_____ | MIT | data/Scraping Fantasy Football Data - FINAL-Week3.ipynb | zgscherrer/Project-Fantasy-Football |
Initial Database Stuff | # actual_ppr_df = pd.read_pickle('pickle_archive/Week1_Player_Actual_PPR_2018-9-13-6-41.pkl')
# espn_final_df = pd.read_pickle('pickle_archive/Week1_PPR_Projections_ESPN_2018-9-13-6-46.pkl')
# cbs_final_df = pd.read_pickle('pickle_archive/Week1_PPR_Projections_CBS_2018-9-13-17-45.pkl')
# cbs_final_df.head()
# from sqla... | _____no_output_____ | MIT | data/Scraping Fantasy Football Data - FINAL-Week3.ipynb | zgscherrer/Project-Fantasy-Football |
Basics of CodingIn this chapter, you'll learn about the basics of objects, types, operations, conditions, loops, functions, and imports. These are the basic building blocks of almost all programming languages.This chapter has benefited from the excellent [*Python Programming for Data Science*](https://www.tomasbeuzen.... | a = 10
print(a) | _____no_output_____ | MIT | code-basics.ipynb | lukestein/coding-for-economists |
This creates a variable `a`, assigns the value 10 to it, and prints it. Sometimes you will hear variables referred to as *objects*. Everything that is not a literal value, such as `10`, is an object. In the above example, `a` is an object that has been assigned the value `10`.How about this: | b = "This is a string"
print(b) | _____no_output_____ | MIT | code-basics.ipynb | lukestein/coding-for-economists |
It's the same thing but with a different **type** of data, a string instead of an integer. Python is *dynamically typed*, which means it will guess what type of variable you're creating as you create it. This has pros and cons, with the main pro being that it makes for more concise code.```{admonition} ImportantEveryth... | list_example = [10, 1.23, "like this", True, None]
print(list_example) | _____no_output_____ | MIT | code-basics.ipynb | lukestein/coding-for-economists |
is completely valid code. `None` is a special type of nothingness, and represents an object with no value. It has type `NoneType` and is more useful than you might think! As well as the built-in types, packages can define their own custom types. If you ever want to check the type of a Python variable, you can call the ... | type(list_example) | _____no_output_____ | MIT | code-basics.ipynb | lukestein/coding-for-economists |
This is especially useful for debugging `ValueError` messages.Below is a table of common data types in Python:| Name | Type name | Type Category | Description | Example || :-------------------- | :--------- | :------------- | :-------------... | list_example.append("one more entry")
print(list_example) | _____no_output_____ | MIT | code-basics.ipynb | lukestein/coding-for-economists |
And you can access earlier entries using an index, which begins at 0 and ends at one less than the length of the list (this is the convention in many programming languages). For instance, to print specific entries at the start, using `0`, and end, using `-1`: | print(list_example[0])
print(list_example[-1]) | _____no_output_____ | MIT | code-basics.ipynb | lukestein/coding-for-economists |
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