markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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What's the most recent activity available?Not sure this is useful. | q(f'''
select max(from_iso8601_timestamp(datetime)) as most_recent_activity
from incoming_rotations
where year = year(current_date) and month >= month(current_date)-1
''') | _____no_output_____ | MIT | notebooks/query_prototyping.ipynb | parente/honey.data |
How has she progressed over the last 7 days? (cumulative plot, origin at sum before period)This should be the total sum prior to the window of interest. The query scans everything instead of trying to skip scanning 7 day partitions--a drop in the bucket. | qid, resp = q('''
select sum(rotations) as prior_rotations
from incoming_rotations
where from_iso8601_timestamp(datetime) < (current_date - interval '7' day)
''')
resp
publish(qid, 'prior-7-day-window.csv'); | _____no_output_____ | MIT | notebooks/query_prototyping.ipynb | parente/honey.data |
And this should be the sum of rotations by hour and the cumulative sum by hour within the window of interest. I'm trying to constrain the search space for the necessary data using partitions. I need a bit more data to make sure this is working properly. | qid, resp = q(f'''
select
sum(rotations) as sum_rotations,
to_iso8601(date_trunc('hour', from_iso8601_timestamp(datetime))) as datetime_hour,
sum(sum(rotations)) over (
order by date_trunc('hour', from_iso8601_timestamp(datetime)) asc
rows between unbounded preceding and current row
) a... | _____no_output_____ | MIT | notebooks/query_prototyping.ipynb | parente/honey.data |
Let's work with the CSV forms of these metrics to create a plot. | tz = pytz.timezone('America/New_York')
resp = s3.get_object(Bucket='honey-data-public', Key='prior-7-day-window.csv')
prior_df = pd.read_csv(resp['Body'])
try:
offset = prior_df.iloc[0].iloc[0]
except:
offset = 0
resp = s3.get_object(Bucket='honey-data-public', Key='7-day-window.csv')
week_df = pd.read_csv(resp... | _____no_output_____ | MIT | notebooks/query_prototyping.ipynb | parente/honey.data |
Filling missing values is something I might want to do in Athena instead of relying on the frontend web client doing it if plot interpolation doesn't look pretty. Some techniques here: https://www.reddit.com/r/SQL/comments/80t1db/inserting_dates_between_a_start_date_and_enddate/ | cumsum_df = week_df[['cumsum_rotations']] + offset
#cumsum_df = cumsum_df.reindex(pd.date_range(week_df.index.min(), week_df.index.max(), freq='1h'), method='ffill')
cumsum_df.index.max() - cumsum_df.index.min()
_, ax = plt.subplots(figsize=(15, 5))
(cumsum_df * wheel_circumference).rename(columns={'cumsum_rotations': ... | _____no_output_____ | MIT | notebooks/query_prototyping.ipynb | parente/honey.data |
How far has she run each night for the past year?We should subtract 12 hours to sum rotations for nocturnal sessions, plus a few hours more to account for the fact that the hamster is in EST/EDT. Then we add one day back to the date to align with end of session reporting used elsewhere. Don't bother getting precisely ... | qid, resp = q(f'''
select
sum(rotations) as value,
date(date_trunc('day', from_iso8601_timestamp(datetime) - interval '16' hour)) + interval '1' day as day
from incoming_rotations
where year >= year(current_date)-1
group by date_trunc('day', from_iso8601_timestamp(datetime) - interval '16' hour)
order by day
''... | _____no_output_____ | MIT | notebooks/query_prototyping.ipynb | parente/honey.data |
What city might she have reached by traveling this distance?https://rapidapi.com/wirefreethought/api/geodb-cities?endpoint=5aadab87e4b00687d35767b4 allows 1000 request per day. If the data upload / aggregation job runs every 10 minutes, I only need about a tenth of that. | rapid_key = getpass.getpass('Rapid API key:')
durham_lat = '35.994034'
durham_lon = '-78.898621'
rapid_url = "https://wft-geo-db.p.rapidapi.com"
def furthest_poi(lat, lon, radius, api_key, base_url=rapid_url):
path = f'/v1/geo/locations/{durham_lat_lon}/nearbyCities'
# Results sort nearest to farthest
resp... | _____no_output_____ | MIT | notebooks/query_prototyping.ipynb | parente/honey.data |
PRÉ-PROCESSAMENTO | Antes['CS_GESTANT'].replace({1.0: 1, 2.0: 1, 3.0 :1, 4.0 : 1}, inplace= True)
Antes['CS_GESTANT'].replace({5.0: 0, 6.0:0, 9.0:0}, inplace= True)
Antes['CS_RACA'].fillna(9,inplace= True)
Antes['CS_ESCOL_N'].fillna(9,inplace= True)
Antes['SURTO_SG'].replace({2.0: 0, 9.0: 0}, inplace= True)
Antes['SURTO_SG'].fillna(0,inp... | _____no_output_____ | MIT | Notebooks cidades/Manaus_Antes.ipynb | amandacaravieri/ProjetoFinal-COVID_Brasil |
- Resetando o Index novamente. | Antes= Antes.reset_index(drop=True)
Antes.head() | _____no_output_____ | MIT | Notebooks cidades/Manaus_Antes.ipynb | amandacaravieri/ProjetoFinal-COVID_Brasil |
- Aplicação da Dummy nas Features Categóricas | Antes=pd.get_dummies(Antes, columns=['CS_SEXO', 'CS_GESTANT', 'CS_RACA', 'CS_ESCOL_N',
'SURTO_SG', 'NOSOCOMIAL', 'FEBRE', 'TOSSE', 'GARGANTA', 'DISPNEIA',
'DESC_RESP', 'SATURACAO', 'DIARREIA', 'VOMITO', 'PUERPERA',
'FATOR_RISC', 'CARDIOPATI', 'HEMATOLOGI', 'SIND_DOWN', 'HEPATICA',
'ASMA', 'D... | _____no_output_____ | MIT | Notebooks cidades/Manaus_Antes.ipynb | amandacaravieri/ProjetoFinal-COVID_Brasil |
Verificando o Balanceamento | Antes["EVOLUCAO"].value_counts(normalize=True)
X = Antes[['IDADE_ANOS','CS_SEXO_M','CS_RACA_4.0','FEBRE_1.0','DISPNEIA_1.0','SATURACAO_1.0','UTI_1.0',
'SUPORT_VEN_1.0', 'SUPORT_VEN_2.0', 'PCR_RESUL_2.0','TOSSE_1.0','DESC_RESP_1.0', 'FATOR_RISC_2']]
y = Antes['EVOLUCAO']
Xtrain, Xtest, ytrain, ytest =... | _____no_output_____ | MIT | Notebooks cidades/Manaus_Antes.ipynb | amandacaravieri/ProjetoFinal-COVID_Brasil |
Aplicação do Modelo Escolhido | random_state=42
RDF = RandomForestClassifier()
RDF.fit(Xtrain_over, ytrain_over)
previsoes = RDF.predict(Xtest_over)
previsoes
accuracy_score(ytest_over, previsoes)
# Testar Modelo
idade = 43.0
sexo = 1
raca = 0
febre = 1
dispneia = 1
saturacao = 0
uti = 1
suport1 = 1
suport2 = 0
pcr = 1
tosse = 1
descresp = 0
frisc =... | [2.]
| MIT | Notebooks cidades/Manaus_Antes.ipynb | amandacaravieri/ProjetoFinal-COVID_Brasil |
Ye ab tak ka code: | import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
def process_image(frame):
def cal_undistort(img):
# Reads mtx and dist ma... | _____no_output_____ | MIT | Pipeline progression/13_variance-weighted-done.ipynb | animesh-singhal/Project-2 |
Let's try classes | # Define a class to receive the characteristics of each line detection
class Line():
def __init__(self):
#Let's count the number of consecutive frames
self.count = 0
# was the line detected in the last iteration?
self.detected = False
#polynomial coefficients for the most r... | starting count value 0
starting count 0
variance_new 16.411728943874007
First case mein variance ye hai 16.411728943874007
[16.411728943874007, 16.411728943874007, 16.411728943874007, 16.411728943874007, 16.411728943874007]
starting count 1
variance_new 20.454135208135213
[16.411728943874007, 16.411728943874007, 16.411... | MIT | Pipeline progression/13_variance-weighted-done.ipynb | animesh-singhal/Project-2 |
Videoo test | # Define a class to receive the characteristics of each line detection
class Line():
def __init__(self):
#Let's count the number of consecutive frames
self.count = 0
# was the line detected in the last iteration?
self.detected = False
#polynomial coefficients for the most r... | _____no_output_____ | MIT | Pipeline progression/13_variance-weighted-done.ipynb | animesh-singhal/Project-2 |
. | import numpy as np
def modify_array(array, new_value):
if len(array)!=5:
for i in range(0,5):
array.append(new_value)
else:
dump_var=array[0]
array[0]=array[1]
array[1]=array[2]
array[2]=array[3]
array[3]=array[4]
array[4]=new_value
... | _____no_output_____ | MIT | Pipeline progression/13_variance-weighted-done.ipynb | animesh-singhal/Project-2 |
Wczytywanie danych | pwd
df = pd.read_hdf("data/car.h5")
df.shape
df.columns | _____no_output_____ | MIT | day5_hyperopt_xgboost.ipynb | kowalcorp/dw_matrix_car |
Dummy model | df.select_dtypes(np.number).columns
feats = ['car_id']
X = df[ feats ].values
y = df[ ['car_id'] ].values
model = DummyRegressor()
model.fit(X,y)
y_pred = model.predict(X)
mae(y, y_pred)
[x for x in df.columns if 'price' in x]
df['price_currency'].value_counts()
df['price_currency'].value_counts(normalize=True)*100
... | _____no_output_____ | MIT | day5_hyperopt_xgboost.ipynb | kowalcorp/dw_matrix_car |
Decision Tree | model = DecisionTreeRegressor(max_depth=5)
run_model(model,cat_feats) | _____no_output_____ | MIT | day5_hyperopt_xgboost.ipynb | kowalcorp/dw_matrix_car |
Random Forest | model = RandomForestRegressor(max_depth=5 , n_estimators=50, random_state=0)
run_model(model,cat_feats) | _____no_output_____ | MIT | day5_hyperopt_xgboost.ipynb | kowalcorp/dw_matrix_car |
XGBoost | xgb_params = {
'max_depth' : 5 ,
'n_estimators' : 50,
'learning_rate' : 0.1,
'seed':0
}
model = xgb.XGBRegressor(**xgb_params)
run_model(model, cat_feats)
xgb_params = {
'max_depth' : 5 ,
'n_estimators' : 50,
'learning_rate' : 0.1,
'seed':0
}
m = xgb.XGBRegressor(**xgb_params)
m.f... | _____no_output_____ | MIT | day5_hyperopt_xgboost.ipynb | kowalcorp/dw_matrix_car |
Dies ist bisher mehr eine Widget Demo als eine Erklärung der Normalverteilung. Aber was nicht ist, kann ja noch werden.In this app we:* Plot the gaussian density for a specific $\mu$ and $\sigma$* Use the FloatSlider widget in ipywidgets to represent $\mu$ and $\sigma$ values* Stack the density plot along with the slid... | import numpy as np
from scipy.stats import norm
from ipywidgets import FloatSlider, HBox, VBox
import bqplot.pyplot as plt
x = np.linspace(-10, 10, 200)
y = norm.pdf(x)
# plot the gaussian density
title_tmpl = 'Gaussian Density (mu = {} and sigma = {})'
pdf_fig = plt.figure(title=title_tmpl.format(0, 1))
pdf_line = p... | _____no_output_____ | BSD-3-Clause | index.ipynb | janbucher/normalverteilung |
IntroductionLast time we have used Lagrange basis to interpolate polynomial. However, it is not efficient to update the interpolating polynomial when a new data point is added. We look at an iterative approach.Given points $\{(z_i, f_i) \}_{i=0}^{n-1}$, $z_i$ are distinct and $p_{n-1} \in \mathbb{C}[z]_{n-1}\, , p_{n-... | z0 = -1; f0 = -3; z1 = 0; f1 = -1; z2 = 2; f2 = 4; z3 = 5; f3 = 1; z4 = 1; f4 = 1
p3 = -13*x**3/90 + 14*x**2/45 + 221*x/90 - 1 | _____no_output_____ | MIT | M2AA3/M2AA3-Polynomials/Lesson 02 - Newton Method/.ipynb_checkpoints/Newton Tableau-checkpoint.ipynb | ImperialCollegeLondon/Random-Stuff |
We add a point $(z_4,f_4) = (1,1)$ and obtain $p_4(x)$ | z4 = 1; f4 = 1
C = (f4 - p3.subs(x,z4))/((z4-z0)*(z4-z1)*(z4-z2)*(z4-z3))
C
p4 = p3 + C*(x-z0)*(x-z1)*(x-z2)*(x-z3)
sp.expand(p4) | _____no_output_____ | MIT | M2AA3/M2AA3-Polynomials/Lesson 02 - Newton Method/.ipynb_checkpoints/Newton Tableau-checkpoint.ipynb | ImperialCollegeLondon/Random-Stuff |
**Remark:** the constant $C$ is usually written as $f[z_0,z_1,z_2,z_3,z_4]$. Moreover by iteration we have$$p_n(z) = \sum_{i=0}^n f[z_0,...,z_n] \prod_{j=0}^i (z - z_j)$$ Newton Tableau We look at efficient ways to compute $f[z_0,...,z_n]$, iteratively from $f[z_0,...,z_{n-1}]$ and $f[z_1,...,z_n]$. We may first const... | def product(xs,key,i):
#Key: Forward or Backward
n = len(xs)-1
l = 1
for j in range(i):
if key == 'forward':
l *= (x - xs[j])
else:
l *= (x - xs[n-j])
return l
def newton(xs,ys,key):
# Key: Forward or Backward
n = len(xs)-1
... | [0.8414709848078965, 0.9719379013633127, 0.9954079577517649, 0.9092974268256817]
[0.3914007496662487, 0.07041016916535667, -0.25833159277824974]
[-0.481485870751338, -0.4931126429154095]
[-0.011626772164071542]
| MIT | M2AA3/M2AA3-Polynomials/Lesson 02 - Newton Method/.ipynb_checkpoints/Newton Tableau-checkpoint.ipynb | ImperialCollegeLondon/Random-Stuff |
INF6804 Vision par ordinateurPolytechnique MontréalExemple de la segmentation d'une image | import numpy as np
import cv2
import os
import matplotlib.pyplot as plt | _____no_output_____ | MIT | ColorSegmentation.ipynb | gabilodeau/INF6804 |
Lecture d'une image. | image_name = 'bureau.jpg'
if not os.path.exists(image_name):
!gdown https://raw.githubusercontent.com/gabilodeau/INF6804/master/images/bureau.jpg
image = cv2.imread(image_name)
b,g,r = cv2.split(image) #OpenCV lit les images en BGR
image = cv2.merge([r,g,b])
#ou image = image[:,:,::-1]
plt.figure(figsize = (8,8))
plt... | _____no_output_____ | MIT | ColorSegmentation.ipynb | gabilodeau/INF6804 |
Segmentation avec K-means. Segmentation en incluant la position des pixels dans l'image. On fera le groupement de vecteurs [R,G,B,X,Y] La plupart des pixels d'un groupe seront connectés entre eux.Cas 1: Sous-segmentation, pas assez de groupes | # Nombre de groupes
K = 6
# Définition des critères d'arret. EPS est le déplacement des centres
criteresArret = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
RGBPix = image.reshape((-1,3))
# Pour ajouter la position. Groupement de vecteur 1 par 5 plutôt que 1 par 3.
RGBetPosPix= np.zeros((len(RGBPix... | _____no_output_____ | MIT | ColorSegmentation.ipynb | gabilodeau/INF6804 |
Cas 2: Sur-segmentation, trop de groupes | K = 100
ret,etiquettes,centres=cv2.kmeans(RGBetPosPix,K,None,criteresArret,1,cv2.KMEANS_RANDOM_CENTERS)
centres = np.uint8(centres)
centressansPos= np.zeros((K,3),dtype=np.uint8) #Pour affichage des classes
for i in range(0,len(centres)):
centressansPos[i]=centres[i][0:3]
res = centressansPos[etiquettes]
res2 = ... | _____no_output_____ | MIT | ColorSegmentation.ipynb | gabilodeau/INF6804 |
Difficile d'avoir un résultat parfait. | _____no_output_____ | MIT | ColorSegmentation.ipynb | gabilodeau/INF6804 | |
resultというディレクトリにtrain_mnist.pyの実行結果が出力されているとします。 | ls result | _____no_output_____ | MIT | 2/7_chainermn_result.ipynb | rymzt/aaic_gathering |
cg.dotはDOT言語で記述されたネットワーク構造のファイル、logはJSONで記述された実行時間、エポック数、反復回数、精度などを記述したファイルになります。 cg.dotはdotコマンドによりpngなどの画像ファイルに変換することができます。 | %%bash
dot -Tpng result/cg.dot -o result/cg.png | _____no_output_____ | MIT | 2/7_chainermn_result.ipynb | rymzt/aaic_gathering |
result/cg.pngを表示してみます。ここではPythonスクリプトを使用して表示してみます。(eogコマンド等を使っても大丈夫です。) | from PIL import Image
from matplotlib import pylab as plt
%matplotlib inline
plt.figure(figsize=(8, 8), dpi=400)
plt.imshow(np.array(Image.open('result/cg.png'))) | _____no_output_____ | MIT | 2/7_chainermn_result.ipynb | rymzt/aaic_gathering |
result/logを表示してみます。Pythonスクリプトを使ってファイルを読み込んでみましょう。 | import json
with open('result/log', 'r') as f:
data = json.load(f)
print(data) | _____no_output_____ | MIT | 2/7_chainermn_result.ipynb | rymzt/aaic_gathering |
epoch毎にelapsed_time, iteration, main/loss, main/accuracy, validation/main/loss, validation/main/accuracyが出力されているのがわかります。ここでは、epoch毎のvalidation/main/accuracyを表示してみましょう。 | x, y = [],[]
xlabel, ylabel = 'epoch', 'validation/main/accuracy'
for d in data:
x.append(d[xlabel])
y.append(d[ylabel])
%matplotlib inline
plt.xlabel(xlabel)
plt.ylabel(ylabel)
plt.plot(x,y) | _____no_output_____ | MIT | 2/7_chainermn_result.ipynb | rymzt/aaic_gathering |
Note* Instructions have been included for each segment. You do not have to follow them exactly, but they are included to help you think through the steps. | # Dependencies and Setup
import pandas as pd
# File to Load (Remember to Change These)
file_to_load = "Resources/purchase_data.csv"
# Read Purchasing File and store into Pandas data frame
purchase_data = pd.read_csv(file_to_load)
purchase_data.head() | _____no_output_____ | Apache-2.0 | HeroesOfPymoli_starter.ipynb | JarrodCasey/pandas-challenge |
Player Count * Display the total number of players | # Calculate the number of unique players in the DataFram
PlayerCount = len(purchase_data["SN"].unique())
# Place data found into a DataFrame
TotalPlayer_df = pd.DataFrame({"Total Players":[PlayerCount]})
TotalPlayer_df | _____no_output_____ | Apache-2.0 | HeroesOfPymoli_starter.ipynb | JarrodCasey/pandas-challenge |
Purchasing Analysis (Total) * Run basic calculations to obtain number of unique items, average price, etc.* Create a summary data frame to hold the results* Optional: give the displayed data cleaner formatting* Display the summary data frame | #Calculate number of unique items
TotUniqueItems = len(purchase_data["Item Name"].unique())
#Calculate average price
AvePrice = purchase_data["Price"].mean()
#Calculate Number of Purchases
TotalPurch = len(purchase_data["Item Name"])
#Calculate Total Revenue
TotRev = purchase_data["Price"].sum()
#Create Data Frame... | _____no_output_____ | Apache-2.0 | HeroesOfPymoli_starter.ipynb | JarrodCasey/pandas-challenge |
Gender Demographics * Percentage and Count of Male Players* Percentage and Count of Female Players* Percentage and Count of Other / Non-Disclosed | #Calculate No. male players
MalePlayer_df = purchase_data[purchase_data['Gender']=="Male"]
TotMalePlayers = len(MalePlayer_df["SN"].unique())
#Calculate Percentage of Male Players
PercMale = TotMalePlayers/PlayerCount
#Calculate No. female players
FemalePlayer_df = purchase_data[purchase_data['Gender']=="Female"]
TotF... | _____no_output_____ | Apache-2.0 | HeroesOfPymoli_starter.ipynb | JarrodCasey/pandas-challenge |
Purchasing Analysis (Gender) * Run basic calculations to obtain purchase count, avg. purchase price, avg. purchase total per person etc. by gender* Create a summary data frame to hold the results* Optional: give the displayed data cleaner formatting* Display the summary data frame | #Calculate No. purchases by females
FemalePurch = len(FemalePlayer_df["SN"])
#Calculate Average Purchase Price by female
FemaleAvePurch = FemalePlayer_df["Price"].mean()
#Calculate Total Purchase Value by Female
FemaleTotPurch = FemalePlayer_df["Price"].sum()
#Calculate averae total purchase per person
FemalePPAvePurch... | _____no_output_____ | Apache-2.0 | HeroesOfPymoli_starter.ipynb | JarrodCasey/pandas-challenge |
Age Demographics * Establish bins for ages* Categorize the existing players using the age bins. Hint: use pd.cut()* Calculate the numbers and percentages by age group* Create a summary data frame to hold the results* Optional: round the percentage column to two decimal points* Display Age Demographics Table | #Create age range bins
bins = [0,9,14,19,24,29,34,39,10000]
#Create labels for bins
group_labels = ["<10","10-14","15-19","20-24","25-29","30-34","35-39","40+"]
#Slice the data and place it into bins and Place data series into a new column inside of the DataFrame
purchase_data["Age Range"] = pd.cut(purchase_data["Age... | _____no_output_____ | Apache-2.0 | HeroesOfPymoli_starter.ipynb | JarrodCasey/pandas-challenge |
Purchasing Analysis (Age) * Bin the purchase_data data frame by age* Run basic calculations to obtain purchase count, avg. purchase price, avg. purchase total per person etc. in the table below* Create a summary data frame to hold the results* Optional: give the displayed data cleaner formatting* Display the summary d... | #Create a second GroupBy object based on "Age Range"
PurchByAgeRg_Group2 = purchase_data.groupby("Age Range")["Price"].agg(["count","mean","sum"])
PurchByAgeRg_Group3 = purchase_data.groupby("Age Range")["SN"].agg(["nunique"])
PurchAnalAge_df= pd.concat([PurchByAgeRg_Group2,PurchByAgeRg_Group3],axis=1,join="inner")
Pur... | _____no_output_____ | Apache-2.0 | HeroesOfPymoli_starter.ipynb | JarrodCasey/pandas-challenge |
Top Spenders * Run basic calculations to obtain the results in the table below* Create a summary data frame to hold the results* Sort the total purchase value column in descending order* Optional: give the displayed data cleaner formatting* Display a preview of the summary data frame | #Create a GroupBy object based on "SN"
PurchBySN_gp = purchase_data.groupby("SN")["Price"].agg(["count","mean","sum"])
#Sort the total purchase value column in descending order
TotalSpender = PurchBySN_gp.sort_values("sum", ascending=False)
#Rename Columns
TotalSpender=TotalSpender.rename(columns={"count":"Purchase C... | _____no_output_____ | Apache-2.0 | HeroesOfPymoli_starter.ipynb | JarrodCasey/pandas-challenge |
Most Popular Items * Retrieve the Item ID, Item Name, and Item Price columns* Group by Item ID and Item Name. Perform calculations to obtain purchase count, average item price, and total purchase value* Create a summary data frame to hold the results* Sort the purchase count column in descending order* Optional: give ... | #Create a GroupBy object based on "SN"
ItemsAnal_gp = purchase_data.groupby(["Item ID","Item Name"])["Price"].agg(["count","mean","sum"])
#Sort the total purchase value column in descending order
ItemsAnalDesc_gp = ItemsAnal_gp.sort_values("count", ascending=False)
#Rename Columns
ItemsAnalDesc_gp=ItemsAnalDesc_gp.re... | _____no_output_____ | Apache-2.0 | HeroesOfPymoli_starter.ipynb | JarrodCasey/pandas-challenge |
Most Profitable Items * Sort the above table by total purchase value in descending order* Optional: give the displayed data cleaner formatting* Display a preview of the data frame | #Sort the total purchase value column in descending order
ItemsAnalTotDesc_gp = ItemsAnalDesc_gp.sort_values("Total Purchase Value", ascending=False)
#Set correct numbering format
ItemsAnalTotDesc_gp.style.format({"Item Price":"${:20,.2f}",
"Total Purchase Value":"${:20,.2f}"}) | _____no_output_____ | Apache-2.0 | HeroesOfPymoli_starter.ipynb | JarrodCasey/pandas-challenge |
EvalutaionTo be able to make a statement about the performance of a question-asnwering system, it is important to evalute it. Furthermore, evaluation allows to determine which parts of the system can be improved. Start an Elasticsearch serverYou can start Elasticsearch on your local machine instance using Docker. If ... | # Recommended: Start Elasticsearch using Docker
! docker run -d -p 9200:9200 -e "discovery.type=single-node" elasticsearch:7.6.2
# In Colab / No Docker environments: Start Elasticsearch from source
#! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.2-linux-x86_64.tar.gz -q
#! tar -xzf elasti... | 05/19/2020 09:03:37 - INFO - elasticsearch - POST http://localhost:9200/_bulk [status:200 request:0.796s]
05/19/2020 09:03:38 - INFO - elasticsearch - POST http://localhost:9200/_bulk [status:200 request:0.222s]
| Apache-2.0 | tutorials/Tutorial5_Evaluation.ipynb | vchulski/haystack |
Initialize components of QA-System | # Initialize Retriever
from haystack.retriever.elasticsearch import ElasticsearchRetriever
retriever = ElasticsearchRetriever(document_store=document_store)
# Initialize Reader
from haystack.reader.farm import FARMReader
reader = FARMReader("deepset/roberta-base-squad2")
# Initialize Finder which sticks together Read... | _____no_output_____ | Apache-2.0 | tutorials/Tutorial5_Evaluation.ipynb | vchulski/haystack |
Evaluation of Retriever | # Evaluate Retriever on its own
retriever_eval_results = retriever.eval()
## Retriever Recall is the proportion of questions for which the correct document containing the answer is
## among the correct documents
print("Retriever Recall:", retriever_eval_results["recall"])
## Retriever Mean Avg Precision rewards retrie... | 05/19/2020 09:04:11 - INFO - elasticsearch - GET http://localhost:9200/feedback/_search?scroll=5m&size=1000 [status:200 request:0.090s]
05/19/2020 09:04:11 - INFO - elasticsearch - GET http://localhost:9200/eval_document/_search [status:200 request:0.051s]
05/19/2020 09:04:11 - INFO - haystack.retriever.elasticsear... | Apache-2.0 | tutorials/Tutorial5_Evaluation.ipynb | vchulski/haystack |
Evaluation of Reader | # Evaluate Reader on its own
reader_eval_results = reader.eval(document_store=document_store, device=device)
# Evaluation of Reader can also be done directly on a SQuAD-formatted file
# without passing the data to Elasticsearch
#reader_eval_results = reader.eval_on_file("../data/natural_questions", "dev_subset.json",... | 05/19/2020 09:04:22 - INFO - elasticsearch - GET http://localhost:9200/feedback/_search?scroll=5m&size=1000 [status:200 request:0.007s]
05/19/2020 09:04:22 - INFO - elasticsearch - GET http://localhost:9200/_search/scroll [status:200 request:0.003s]
05/19/2020 09:04:22 - INFO - elasticsearch - DELETE http://local... | Apache-2.0 | tutorials/Tutorial5_Evaluation.ipynb | vchulski/haystack |
Evaluation of Finder | # Evaluate combination of Reader and Retriever through Finder
finder_eval_results = finder.eval()
print("Retriever Recall in Finder:", finder_eval_results["retriever_recall"])
print("Retriever Mean Avg Precision in Finder:", finder_eval_results["retriever_map"])
# Reader is only evaluated with those questions, where ... | 05/19/2020 09:04:57 - INFO - elasticsearch - GET http://localhost:9200/feedback/_search?scroll=5m&size=1000 [status:200 request:0.006s]
05/19/2020 09:04:57 - INFO - elasticsearch - GET http://localhost:9200/eval_document/_search [status:200 request:0.006s]
05/19/2020 09:04:57 - INFO - haystack.retriever.elasticsear... | Apache-2.0 | tutorials/Tutorial5_Evaluation.ipynb | vchulski/haystack |
"US oil & gas production"> "Accessing EIA with Jupyter" In this post, we will plot a chart of (1) US oil production and (2) US gas production by accessing this data from EIA.First, install the python wrapper for Energy Information Administration (EIA) API using your Command Prompt pip install EIA_python | #import customary packages
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
#import EIA package
import eia | _____no_output_____ | Apache-2.0 | _notebooks/2021-01-22-US oil & gas production with EIA.ipynb | ujpradhan/blog |
define key using your personal EIA API key key = eia.API("Peronal API KEY") | #hide_input
key = eia.API('fae9d0bd7f4172e57a1876b2e5802392')
#Let's try quering EIA with our key. EIA has unique series ID for a variety of data.
#Browse series ID here: https://www.eia.gov/opendata/qb.php?category=371
#We'll first query for "U.S. Field Production of Crude Oil, Annual" with "PET.MCRFPUS2.A"
oil = ke... | _____no_output_____ | Apache-2.0 | _notebooks/2021-01-22-US oil & gas production with EIA.ipynb | ujpradhan/blog |
oneTBB Concurrent Containers Sections- [oneTBB Concurrent Containers](oneTBB-Concurrent-Containers)- _Code_: [A Producer-Consumer Application with tbb::concurrent_queue](A-Producer-Consumer-Application-with-tbb::concurrent_queue) Learning Objectives* Learn how thread-unsafe uses of a standard container might be addr... | %%writefile lab/q-serial.cpp
//==============================================================
// Copyright (c) 2020 Intel Corporation
//
// SPDX-License-Identifier: Apache-2.0
// =============================================================
#include <iostream>
#include <queue>
int main() {
int sum (0);
int item;
... | _____no_output_____ | Apache-2.0 | examples/cppcon_2020/04_oneTBB_concurrent_containers/oneTBB_concurrent_containers.ipynb | alvdd/oneTBB |
Build and Run the baselineSelect the cell below and click Run ▶ to compile and execute the code above: | ! chmod 755 q; chmod 755 ./scripts/run_q-serial.sh; if [ -x "$(command -v qsub)" ]; then ./q scripts/run_q-serial.sh; else ./scripts/run_q-serial.sh; fi | _____no_output_____ | Apache-2.0 | examples/cppcon_2020/04_oneTBB_concurrent_containers/oneTBB_concurrent_containers.ipynb | alvdd/oneTBB |
Implement a parallel version with tbb::concurrent_priority_queueIn this section, we modify the example to create one producer threadand two consumer threads that run concurrently. To eliminate the potentialrace on the call to `empty` and `pop`, we replace the `std::priority_queue` with `tbb::concurrent_priority_queue`... | %%writefile lab/q-parallel.cpp
//==============================================================
// Copyright (c) 2020 Intel Corporation
//
// SPDX-License-Identifier: Apache-2.0
// =============================================================
#include <iostream>
#include <queue>
#include <thread>
#include <tbb/tbb.h>
... | _____no_output_____ | Apache-2.0 | examples/cppcon_2020/04_oneTBB_concurrent_containers/oneTBB_concurrent_containers.ipynb | alvdd/oneTBB |
Build and Run the modified codeSelect the cell below and click Run ▶ to compile and execute the code that you modified above: | ! chmod 755 q; chmod 755 ./scripts/run_q-parallel.sh; if [ -x "$(command -v qsub)" ]; then ./q scripts/run_q-parallel.sh; else ./scripts/run_q-parallel.sh; fi | _____no_output_____ | Apache-2.0 | examples/cppcon_2020/04_oneTBB_concurrent_containers/oneTBB_concurrent_containers.ipynb | alvdd/oneTBB |
Producer-Consumer Solution (Don't peak unless you have to) | %%writefile solutions/q-parallel.cpp
//==============================================================
// Copyright (c) 2020 Intel Corporation
//
// SPDX-License-Identifier: Apache-2.0
// =============================================================
#include <iostream>
#include <queue>
#include <thread>
#include <tbb/t... | _____no_output_____ | Apache-2.0 | examples/cppcon_2020/04_oneTBB_concurrent_containers/oneTBB_concurrent_containers.ipynb | alvdd/oneTBB |
Question 1: Given some sample data, write a program to answer the following: On Shopify, we have exactly 100 sneaker shops, and each of these shops sells only one model of shoe. We want to do some analysis of the average order value (AOV). When we look at orders data over a 30 day window, we naively calculate an AOV o... | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Load dataset
shopify_ds = pd.read_csv("2019 Winter Data Science Intern Challenge Data Set - Sheet1.csv", parse_dates = ['created_at'])
shopify_ds
shopify_ds.head() | _____no_output_____ | MIT | Shopify-DataScience-Intern-Challenge-S2022.ipynb | aniruddhashinde29/Shopify-Data-Science-Intern-Summer-2022-Challenge |
Exploratory Data Analysis and Descriptive Statistics | shopify_ds.describe() | _____no_output_____ | MIT | Shopify-DataScience-Intern-Challenge-S2022.ipynb | aniruddhashinde29/Shopify-Data-Science-Intern-Summer-2022-Challenge |
- Mean order value ($3145.12) is crazy high for a sneaker store considering even if the most expensive sneakers are sold from the most sought after brands. | shopify_ds.info() | <class 'pandas.core.frame.DataFrame'>
RangeIndex: 5000 entries, 0 to 4999
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 order_id 5000 non-null int64
1 shop_id 5000 non-null int64
2 u... | MIT | Shopify-DataScience-Intern-Challenge-S2022.ipynb | aniruddhashinde29/Shopify-Data-Science-Intern-Summer-2022-Challenge |
- No nul values in the dataset! | shopify_ds['shop_id'].nunique()
fig = plt.figure(figsize = (10, 10))
sns.boxplot(shopify_ds['order_amount']); | _____no_output_____ | MIT | Shopify-DataScience-Intern-Challenge-S2022.ipynb | aniruddhashinde29/Shopify-Data-Science-Intern-Summer-2022-Challenge |
- Looks like there are a lot of outliers that are affecting the average order value. Lets find out those. | data = shopify_ds['order_amount']
# Function to Detect Outliers
def find_anomalies(data):
#define a list to accumlate anomalies
anomalies = []
# Set upper and lower limit to 3 standard deviation
data_std = np.std(data)
data_mean = np.mean(data)
anomaly_cut_off = data_std * 3
lower... | Lower Limit: -120690.10466525992
Upper Limit: 126980.36066525991
Outliers: [704000, 704000, 704000, 154350, 704000, 704000, 704000, 704000, 704000, 704000, 704000, 704000, 704000, 704000, 704000, 704000, 704000, 704000]
| MIT | Shopify-DataScience-Intern-Challenge-S2022.ipynb | aniruddhashinde29/Shopify-Data-Science-Intern-Summer-2022-Challenge |
- Any value that is beyond 3 standard deviations from the mean is an outlier. Above we have a list of order amounts that fall in the outlier region and because of them the AOV is high for an affordable product like shoes. | shopify_ds.loc[shopify_ds['order_amount'].isin(outliers)] | _____no_output_____ | MIT | Shopify-DataScience-Intern-Challenge-S2022.ipynb | aniruddhashinde29/Shopify-Data-Science-Intern-Summer-2022-Challenge |
Interesting findings here.- All of the outliers amounts are transactions done by two specific users.- They are buying shoes in bulk; 2000 pairs of shoes by user_id 607 and 6 pairs of shoes by user_id 878 which is shooting up the AOV.- We can still consider user id 878's order as legitimate as it is just one transaction... | shopify_ds_user_amount = pd.DataFrame({'mean_amount': shopify_ds.groupby('user_id')['order_amount'].mean()}).reset_index()
shopify_ds_user_amount.sort_values(by = 'mean_amount', ascending = False).head(30) | _____no_output_____ | MIT | Shopify-DataScience-Intern-Challenge-S2022.ipynb | aniruddhashinde29/Shopify-Data-Science-Intern-Summer-2022-Challenge |
- A massive mean amount of $704,000 just by one user using a credit card. That is definitley a case of credit card fraud.- Second and third highest spending by users 878 and 766 is also suspicious which we will take a look at later. | shopify_ds[shopify_ds['user_id'] == 607] | _____no_output_____ | MIT | Shopify-DataScience-Intern-Challenge-S2022.ipynb | aniruddhashinde29/Shopify-Data-Science-Intern-Summer-2022-Challenge |
- User ID 607 made 17 transactions and each time purchased 2000 shoes from shop id 42 worth 70400 dollars, that is total expenditure of $11.96m with a credit card. Fraud alert. Let's remove user Id 607 and plot again. | subset_df = shopify_ds_user_amount[shopify_ds_user_amount['user_id'] != 607]
subset_df.head()
fig = plt.figure(figsize = (20, 10))
plt.bar(subset_df['user_id'], subset_df['mean_amount']);
# Plot for users with mean amount greater tham $2000
subset_df = subset_df[subset_df['mean_amount'] > 2000]
fig = plt.figure(figsiz... | _____no_output_____ | MIT | Shopify-DataScience-Intern-Challenge-S2022.ipynb | aniruddhashinde29/Shopify-Data-Science-Intern-Summer-2022-Challenge |
- User id 878 and 766's spending amount are the ones that stand out in this graph. Let's check. | subset_df[subset_df['user_id'] == 878]
shopify_ds[shopify_ds['user_id'] == 878] | _____no_output_____ | MIT | Shopify-DataScience-Intern-Challenge-S2022.ipynb | aniruddhashinde29/Shopify-Data-Science-Intern-Summer-2022-Challenge |
- User id 878 has a very high one time spending which is through a debit card, shop id is 78. | shopify_ds[shopify_ds['user_id'] == 766] | _____no_output_____ | MIT | Shopify-DataScience-Intern-Challenge-S2022.ipynb | aniruddhashinde29/Shopify-Data-Science-Intern-Summer-2022-Challenge |
- User 766 also has a one time high spending at shop id 78.- Shop id 78 clearly looks suspicious, let us check | shopify_ds[shopify_ds['shop_id'] == 78]
shopify_ds_shop = pd.DataFrame({'mean_amount': shopify_ds.groupby('shop_id')['order_amount'].mean()}).reset_index()
shopify_ds_shop
fig = plt.figure(figsize = (30, 15))
sns.barplot(x = 'shop_id', y = 'mean_amount', data = shopify_ds_shop); | _____no_output_____ | MIT | Shopify-DataScience-Intern-Challenge-S2022.ipynb | aniruddhashinde29/Shopify-Data-Science-Intern-Summer-2022-Challenge |
- Another case of fraud at shop with id 42 along with 78. | shopify_ds[shopify_ds['shop_id'] == 42] | _____no_output_____ | MIT | Shopify-DataScience-Intern-Challenge-S2022.ipynb | aniruddhashinde29/Shopify-Data-Science-Intern-Summer-2022-Challenge |
- Let's remove the 2 fraud cases: user id 607 and shop id 78 and observe the data again. | clean_df = shopify_ds[shopify_ds['user_id' ]!= 607]
clean_df = clean_df[clean_df['shop_id'] != 78]
clean_df
clean_df.describe() | _____no_output_____ | MIT | Shopify-DataScience-Intern-Challenge-S2022.ipynb | aniruddhashinde29/Shopify-Data-Science-Intern-Summer-2022-Challenge |
CLASSES 1 Object-oriented programming We now turn our attention to the **object-oriented programming** topic. The OOP is a programming paradigm based on the concept of **objects**: contain * **attributes(data)** and * the **methods(operations)** that operate on that attributes.>Formally, an object is a collection of ... | class Person:
def __init__(self, name, age):
self.name = name
self.age = age
def myfunc(self):
print("Hello my name is " + self.name)
p1 = Person("Li",56)
p1.myfunc()
p2=p1
p2.age
p2.age=44
p1.age | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
2.1 Create an `object` of `class` type use the `class` keyword to define a new type class:`Circle`* a subclass of `object````pythonclass Person: ``` | print(type(Person)) | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
2.2 Creates a set of `attributes` and `methods`An class contains * **attributes** * think of data as other objects that make up the class * **methods** * think of methods as functions that only work with this class * how to interact with the object**Access any attribute**The dot **“`.`”** operator is used t... | p1.myfunc() | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
3 The Magic Method `__str__` Add the Magic Method `__str__` to the class Circle>**Magic Method：**>>One of the design goals for Python was to allow programmers to use classes to define new types that are as easy to **use as the `built-in` types** of Python. >>Using magic methods to provide **class-specific definition... | class Person:
def __init__(self, name, age):
self.name = name
self.age = age
def myfunc(self):
print("Hello my name is " + self.name)
def __str__(self):
"""Returns a string representation of Person"""
return f"The age of {self.name} is {self.age}" | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
3.1 the `print` commandthe **`__str__`** function associated with the object to be `printed` is **automatically invoked** | p1=Person("zhang shan",21)
#p1.__str__()
print(p1) | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
3.2 calling `str`the `__str__` function is automatically invoked to convert a instance of that class a string | str(p1)
p1.__str__() | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
3.3 Build-in `__str__`* List,dict,tuple | l=[1,2,3]
print(l)
str(l)
l.__str__()
d={'a':1,'b':2}
print(d)
str(d)
d.__str__()
t=('a',1,'c')
print(t)
str(t)
t.__str__() | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
4 Inheritance**Inheritance** provides a convenient mechanism for building **groups of `related` abstractions**It allows programmers to create a type hierarchy in which each type inherits attributes from the types above it in the hierarchy.```pythonclass subclass(superclass):``` 4.1 The class Student` We shall define ... | class Student(Person):
next_id_num = 0 #identification number
def __init__(self, name,age):
super().__init__(name,age)
self.id_num = Student.next_id_num
Student.next_id_num += 1
def __str__(self):
"""Returns a string representation of Student"""
return ... | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
The subclass Student add **new** attributes: * **the class variable(类变量)**: * `_next_id_num`, belongs to the class` Student`, rather than to instances of the class.>* belongs to the class>* shared by all instance of the classs* **the instance variable(实例变量)**: * `_id_num`： id of each cyclinder instanceoverride met... | s1 = Student(name="Li Shi",age=22)
print(s1) | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
**Class variable*** belongs to the class* shared by all instance of the classs | print(Student.next_id_num) # belongs to the class
print(s1.next_id_num) # shared by all instance of the classs | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
**s2 = Student(name="wang yi", age=20)** | s2 =Student("wang yi", 20)
print(s2) | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
5 Private Variables and Methods in PythonPython does not have keywords to access control.In other words,* All attributes and methods are PUBLIC by default in PythonBy convention, * Names begin with **double underscores (`__`)** and **not end with double underscores** are further hidden from direct access | class Student(Person):
next_id_num = 0 #identification number
def __init__(self, name,age):
super().__init__(name,age)
self.id_num = Student.next_id_num
Student.next_id_num += 1
def add_grades(self,grades):
self.grades=grades
self.avg_grade=self.__ge... | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
**Public** | s3.add_grades([100,90,80,50])
s3.ave_grade | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
**Private** | s3.__get_average_grade()
s3.__min_grade | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
6 The UML class diagramThe [Unified Modeling Language(UML统一建模语言)](https://en.wikipedia.org/wiki/Unified_Modeling_Language) is a general-purpose, developmental, modeling language in the field of software engineering that is intended to provide a standard way to visualize the design of a system.[A class diagram](https:/... | import iplantuml
%%plantuml
class Person {
+ name: str
+ age: int
+ {static} Person(name:str.age:int)
+ myfunc()
+ __str()__:str
} | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
6.2 The UML class Inheritance 6.2.1 The Class-level(类) relationship: InheritanceIf two classes are in an **inheritance** relation, * the subclass inherits its attributes and methods from the superclass.The UML graphical representation of **an inheritance relation** is **a hollow triangle shape** on the **superclass en... | %%plantuml
class Person {
+ name: str
+ age: int
+ {static} Person(name:str.age:int)
+ myfunc()
+ __str()__:str
}
class Student{
+ {static} next_id_num:int
+ id_num:int
+ grades: float [1..*]
+ ave_grade:float
- __min_grade:float
+ {static} Student(name:str,age:int)
+ add_grades(grades:float [1... | _____no_output_____ | MIT | notebook/Unit3-1-Classes.ipynb | Py03013050/Home-Py03013050 |
**Instructions:**1. Rename the notebook (click on the title at the top of the page next to 'jupyter') to your A number, name and HW2Py: example (replace spaces with underscores -- this seems to work better): "Reid-Otsuji-A1234567-HW2Py"2. Add your code or markdown answers under the question. 3. `File>Download as` a '... | me = ["reid otsuji", 'blue', 'a23456789'] | _____no_output_____ | CC-BY-4.0 | Homework/2018-hw2-python-solutions.ipynb | U2NG/win2020-gps-python |
Print just your name and student ID. You can use a slicing operator or a loop. | me[0::2] | _____no_output_____ | CC-BY-4.0 | Homework/2018-hw2-python-solutions.ipynb | U2NG/win2020-gps-python |
Using the join method, join the list into a string separated by a comma | print('join the list:', ','.join(me)) | join the list: reid otsuji,blue,a23456789
| CC-BY-4.0 | Homework/2018-hw2-python-solutions.ipynb | U2NG/win2020-gps-python |
Writing a loop to generate a list of letters. Use a for-loop to convert the string "global" into a list of letters:["g", "l", "o", "b", "a", "l"]Hint: You can create an empty list like this: | my_list = []
my_list = []
for char in "global":
my_list.append(char)
print(my_list) | ['g', 'l', 'o', 'b', 'a', 'l']
| CC-BY-4.0 | Homework/2018-hw2-python-solutions.ipynb | U2NG/win2020-gps-python |
Write a program that reads in the regional gapminder data sets and plots the average GDP per capita for each region over time in a single chart. | %matplotlib inline
import matplotlib.pyplot as plt
import glob
import pandas | _____no_output_____ | CC-BY-4.0 | Homework/2018-hw2-python-solutions.ipynb | U2NG/win2020-gps-python |
Fill in the blanks for the following code. Remember you want to:1. Loop thru a list of the contintent gdp files in the data/ directory2. Read each of those files in as data.frames3. Get the mean for each4. Plot each 5. Add a legend for each. Notice we are doing this by using a list of the countries in alpha orderNote:... | for filename in glob.____('data/______.csv'):
df = pandas._______(filename)
cont = df.____()
cont.____()
plt.____(rotation=90)
plt.____(['Africa', 'Asia', 'Americas', 'Europe', 'Oceania'])
plt.style.use('seaborn-muted')
for filename in glob.glob('data/gapminder_gdp*.csv'):
df = pandas.read_c... | _____no_output_____ | CC-BY-4.0 | Homework/2018-hw2-python-solutions.ipynb | U2NG/win2020-gps-python |
Encapsulating Data AnalysisRun the following code. This code reads in the gapminder file for asia and then subsets the Japan data into a new dataframe called `japan`. Remember: Use the correct file path for the .csv saved on your computer. | import pandas
df = pandas.read_csv('data/gapminder_gdp_asia.csv', index_col=0) #note: use the file path for your saved data location
japan = df.ix['Japan']
japan.tail(6) | _____no_output_____ | CC-BY-4.0 | Homework/2018-hw2-python-solutions.ipynb | U2NG/win2020-gps-python |
Using the japan data frame we created above, the below code is one way to get the average for Japan in the 80s. We create a year base (198) using floor division and then add the strings 2 and 7 to that to index for the 1982 and 1987 years (the two years we have in the 80s). We add those together and then divide by 2 t... | year = 1983
gdp_decade = 'gdpPercap_' + str(year // 10) #`//` is Floor division gives us 198
avg = (japan.ix[gdp_decade + '2'] + japan.ix[gdp_decade + '7']) / 2 # we want to add 1982 and 1987 and divide by 2
print(avg) | 20880.0238
| CC-BY-4.0 | Homework/2018-hw2-python-solutions.ipynb | U2NG/win2020-gps-python |
Given the code above, abstract the code into a function named `avg_gdp_in_decade` that will take a `country`, `continent` and `year` as parameters and return the average. We should be able to call your function like this: ```{python}avg_gdp_in_decade('Algeria','Africa', 1970) 1970 for the 70s, 1980 would be 80s```I sta... | def avg_dgp_in_decade(country, continent, year):
#read the data in
#create a new data.frame for the country
#get the decade base three numbers 198 for 80s
#subset and calculate avg
#return average
pass # pass tells python to pass on the function, remove it when you want to run your function | _____no_output_____ | CC-BY-4.0 | Homework/2018-hw2-python-solutions.ipynb | U2NG/win2020-gps-python |
Syntaxanalyse durch [Deplacy](https://koichiyasuoka.github.io/deplacy/) mit [Camphr-Udify](https://camphr.readthedocs.io/en/latest/notes/udify.html) | !pip install deplacy camphr 'unofficial-udify>=0.3.0' en-udify@https://github.com/PKSHATechnology-Research/camphr_models/releases/download/0.7.0/en_udify-0.7.tar.gz
import pkg_resources,imp
imp.reload(pkg_resources)
import spacy
nlp=spacy.load("en_udify")
doc=nlp("Er sieht sehr jung aus.")
import deplacy
deplacy.render... | _____no_output_____ | MIT | doc/de.ipynb | kyodocn/deplacy |
mit [Stanza](https://stanfordnlp.github.io/stanza) | !pip install deplacy stanza
import stanza
stanza.download("de")
nlp=stanza.Pipeline("de")
doc=nlp("Er sieht sehr jung aus.")
import deplacy
deplacy.render(doc)
deplacy.serve(doc,port=None)
# import graphviz
# graphviz.Source(deplacy.dot(doc)) | _____no_output_____ | MIT | doc/de.ipynb | kyodocn/deplacy |
mit [COMBO-pytorch](https://gitlab.clarin-pl.eu/syntactic-tools/combo) | !pip install --index-url https://pypi.clarin-pl.eu/simple deplacy combo
import combo.predict
nlp=combo.predict.COMBO.from_pretrained("german-ud27")
doc=nlp("Er sieht sehr jung aus.")
import deplacy
deplacy.render(doc)
deplacy.serve(doc,port=None)
# import graphviz
# graphviz.Source(deplacy.dot(doc)) | _____no_output_____ | MIT | doc/de.ipynb | kyodocn/deplacy |
mit [UDPipe 2](http://ufal.mff.cuni.cz/udpipe/2) | !pip install deplacy
def nlp(t):
import urllib.request,urllib.parse,json
with urllib.request.urlopen("https://lindat.mff.cuni.cz/services/udpipe/api/process?model=de&tokenizer&tagger&parser&data="+urllib.parse.quote(t)) as r:
return json.loads(r.read())["result"]
doc=nlp("Er sieht sehr jung aus.")
import deplac... | _____no_output_____ | MIT | doc/de.ipynb | kyodocn/deplacy |
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