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 |
|---|---|---|---|---|---|
US Treasury Interest Rates / Yield Curve Data---A look at the US Treasury yield curve, according to interest rates published by the US Treasury. | import pandas as pd
import altair as alt
import numpy as np
url = 'https://www.treasury.gov/resource-center/data-chart-center/interest-rates/pages/TextView.aspx?data=yieldYear&year={year}'
def fetchRates(year):
df = pd.read_html(url.format(year=year), skiprows=0, attrs={ "class": "t-chart" })[0]
df['Date'] = ... | _____no_output_____ | MIT | analysis/us-treasury-rates.ipynb | pymoment/us-macro |
How do the interest rates look for the past 4 years (by instrument)? | years = range(2016, 2022)
fields = ['Date', '3 mo', '1 yr', '2 yr', '7 yr', '10 yr']
dfm = fetchTsRates(years)[fields].melt(id_vars='Date', var_name='Maturity')
alt.Chart(dfm).mark_line().encode(
alt.X('Date:T', axis=alt.Axis(title='')),
alt.Y('value:Q',
axis=alt.Axis(title='Interest Rate [%]'),
... | _____no_output_____ | MIT | analysis/us-treasury-rates.ipynb | pymoment/us-macro |
Same chart as above, just a different mix of instruments | years = range(2016, 2022)
fields = ['Date', '6 mo', '2 yr', '3 yr', '10 yr', '30 yr']
dfm = fetchTsRates(years)[fields].melt(id_vars='Date', var_name='Maturity')
c = alt.Chart(dfm).mark_line().encode(
alt.X('Date:T', axis=alt.Axis(title='')),
alt.Y('value:Q',
axis=alt.Axis(title='Interest Rate [%]')... | _____no_output_____ | MIT | analysis/us-treasury-rates.ipynb | pymoment/us-macro |
How did that chart look for the 4 years before 2008? | years = range(2004, 2010)
fields = ['Date', '6 mo', '2 yr', '3 yr', '10 yr', '30 yr']
dfm2 = fetchTsRates(years)[fields].melt(id_vars='Date', var_name='Maturity')
alt.Chart(dfm2).mark_line().encode(
alt.X('Date:T', axis=alt.Axis(title='', format='%b %Y')),
alt.Y('value:Q',
axis=alt.Axis(title='Inter... | _____no_output_____ | MIT | analysis/us-treasury-rates.ipynb | pymoment/us-macro |
Visualizing the "yield curve" of US Treasuries | years = range(2004, 2009)
instruments = {
0.25: '3 Month T-bill',
0.5: '6 Month T-bill',
2: '2 Year Note',
10: '10 Year Note',
30: '30 Year Bond'
}
fieldsToYears = {'3 mo': 0.25, '6 mo': 0.5, '2 yr': 2, '10 yr': 10, '30 yr': 30}
fields = [i for i in fieldsToYears.keys()]
dfm2 = fetchTsRates(years)[... | _____no_output_____ | MIT | analysis/us-treasury-rates.ipynb | pymoment/us-macro |
Demo of DBSCAN clustering algorithmFinds core samples of high density and expands clusters from them. | print(__doc__)
import numpy as np
from sklearn.cluster import DBSCAN
from sklearn import metrics
from sklearn.datasets.samples_generator import make_blobs
from sklearn.preprocessing import StandardScaler
# #############################################################################
# Generate sample data
centers =... | Automatically created module for IPython interactive environment
| MIT | plot_dbscan.ipynb | roobooot/dbscan |
This notebook is part of the $\omega radlib$ documentation: https://docs.wradlib.org.Copyright (c) $\omega radlib$ developers.Distributed under the MIT License. See LICENSE.txt for more info. Supported radar data formats The binary encoding of many radar products is a major obstacle for many potential radar users. Oft... | import wradlib as wrl
import warnings
#warnings.filterwarnings('ignore')
import matplotlib.pyplot as pl
import numpy as np
try:
get_ipython().magic("matplotlib inline")
except:
pl.ion() | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
German Weather Service: DX format The German Weather Service uses the DX file format to encode local radar sweeps. DX data are in polar coordinates. The naming convention is as follows: raa00-dx_<location-id>-<YYMMDDHHMM>-<location-abreviation>---bin or raa00-dx_<location-id>-<YYYYMMDDHHMM&g... | fpath = 'dx/raa00-dx_10908-0806021655-fbg---bin.gz'
f = wrl.util.get_wradlib_data_file(fpath)
data, metadata = wrl.io.read_dx(f) | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
Here, ``data`` is a two dimensional array of shape (number of azimuth angles, number of range gates). This means that the number of rows of the array corresponds to the number of azimuth angles of the radar sweep while the number of columns corresponds to the number of range gates per ray. | print(data.shape)
print(metadata.keys())
fig = pl.figure(figsize=(10, 10))
ax, im = wrl.vis.plot_ppi(data, fig=fig, proj='cg') | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
German Weather Service: RADOLAN (quantitative) composit The quantitative composite format of the DWD (German Weather Service) was established in the course of the [RADOLAN project](https://www.dwd.de/DE/leistungen/radolan/radolan.html). Most quantitative composite products from the DWD are distributed in this format, ... | fpath = 'radolan/misc/raa01-rw_10000-1408102050-dwd---bin.gz'
f = wrl.util.get_wradlib_data_file(fpath)
data, metadata = wrl.io.read_radolan_composite(f) | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
Here, ``data`` is a two dimensional integer array of shape (number of rows, number of columns). Different product types might need different levels of postprocessing, e.g. if the product contains rain rates or accumulations, you will normally have to divide data by factor 10. ``metadata`` is again a dictionary which pr... | print(data.shape)
print(metadata.keys()) | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
Masking the NoData (or missing) values can be done by: | maskeddata = np.ma.masked_equal(data,
metadata["nodataflag"])
fig = pl.figure(figsize=(10, 8))
# get coordinates
radolan_grid_xy = wrl.georef.get_radolan_grid(900, 900)
x = radolan_grid_xy[:, :, 0]
y = radolan_grid_xy[:, :, 1]
# create quick plot with colorbar and title
pl.figure(figsi... | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
HDF5 OPERA HDF5 (ODIM_H5) [HDF5](https://www.hdfgroup.org/solutions/hdf5/) is a data model, library, and file format for storing and managing data. The [OPERA program](https://www.eumetnet.eu/activities/observations-programme/current-activities/opera/) developed a convention (or information model) on how to store and... | fpath = 'hdf5/knmi_polar_volume.h5'
f = wrl.util.get_wradlib_data_file(fpath)
fcontent = wrl.io.read_opera_hdf5(f) | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
The user should inspect the output obtained from his or her hdf5 file in order to see how access those items which should be further processed. In order to get a readable overview of the output dictionary, one can use the pretty printing module: | # which keyswords can be used to access the content?
print(fcontent.keys())
# print the entire content including values of data and metadata
# (numpy arrays will not be entirely printed)
print(fcontent['dataset1/data1/data']) | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
Please note that in order to experiment with such datasets, you can download hdf5 sample data from the [OPERA](https://www.eumetnet.eu/activities/observations-programme/current-activities/opera/) or use the example data provided with the [wradlib-data](https://github.com/wradlib/wradlib-data/) repository. | fig = pl.figure(figsize=(10, 10))
im = wrl.vis.plot_ppi(fcontent['dataset1/data1/data'], fig=fig, proj='cg') | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
GAMIC HDF5 GAMIC refers to the commercial [GAMIC Enigma MURAN software](https://www.gamic.com) which exports data in hdf5 format. The concept is quite similar to the above [OPERA HDF5 (ODIM_H5)](OPERA-HDF5-(ODIM_H5)) format. **Note** Since $\omega radlib$ version 1.10 an ``GAMIC`` reader [wradlib.io.hdf.open_gamic... | fpath = 'hdf5/2014-08-10--182000.ppi.mvol'
f = wrl.util.get_wradlib_data_file(fpath)
data, metadata = wrl.io.read_gamic_hdf5(f) | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
While metadata represents the usual dictionary of metadata, the data variable is a dictionary which might contain several numpy arrays with the keywords of the dictionary indicating different moments. | print(metadata.keys())
print(metadata['VOL'])
print(metadata['SCAN0'].keys())
print(data['SCAN0'].keys())
print(data['SCAN0']['PHIDP'].keys())
print(data['SCAN0']['PHIDP']['data'].shape)
fig = pl.figure(figsize=(10, 10))
im = wrl.vis.plot_ppi(data['SCAN0']['ZH']['data'], fig=fig, proj='cg') | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
Generic HDF5 This is a generic hdf5 reader, which will read any hdf5 structure. | fpath = 'hdf5/2014-08-10--182000.ppi.mvol'
f = wrl.util.get_wradlib_data_file(fpath)
fcontent = wrl.io.read_generic_hdf5(f)
print(fcontent.keys())
print(fcontent['where'])
print(fcontent['how'])
print(fcontent['scan0/moment_3'].keys())
print(fcontent['scan0/moment_3']['attrs'])
print(fcontent['scan0/moment_3']['data'].... | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
NetCDF The NetCDF format also claims to be self-describing. However, as for all such formats, the developers of netCDF also admit that "[...] the mere use of netCDF is not sufficient to make data self-describing and meaningful to both humans and machines [...]" (see [here](https://www.unidata.ucar.edu/software/netcdf/... | fpath = 'netcdf/example_cfradial_ppi.nc'
f = wrl.util.get_wradlib_data_file(fpath)
outdict = wrl.io.read_generic_netcdf(f)
for key in outdict.keys():
print(key) | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
Please see [this example notebook](wradlib_generic_netcdf_example.ipynb) to get started. EDGE NetCDF EDGE is a commercial software for radar control and data analysis provided by the Enterprise Electronics Corporation. It allows for netCDF data export. The resulting files can be read by [wradlib.io.read_generic_netcdf... | fpath = 'netcdf/edge_netcdf.nc'
f = wrl.util.get_wradlib_data_file(fpath)
data, metadata = wrl.io.read_edge_netcdf(f)
print(data.shape)
print(metadata.keys()) | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
Gematronik Rainbow Rainbow refers to the commercial [RAINBOW®5 APPLICATION SOFTWARE](https://www.leonardogermany.com/en/products/rainbow-5) which exports data in an XML flavour, which due to binary data blobs violates XML standard. Gematronik provided python code for implementing this reader in $\omega radlib$, which ... | fpath = 'rainbow/2013070308340000dBuZ.azi'
f = wrl.util.get_wradlib_data_file(fpath)
fcontent = wrl.io.read_rainbow(f) | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
The user should inspect the output obtained from his or her Rainbow file in order to see how access those items which should be further processed. In order to get a readable overview of the output dictionary, one can use the pretty printing module: | # which keyswords can be used to access the content?
print(fcontent.keys())
# print the entire content including values of data and metadata
# (numpy arrays will not be entirely printed)
print(fcontent['volume']['sensorinfo']) | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
You can check this [example notebook](wradlib_load_rainbow_example.ipynb) for getting a first impression. Vaisala Sigmet IRIS [IRIS](https://www.vaisala.com/en/products/instruments-sensors-and-other-measurement-devices/weather-radar-products/iris-focus) refers to the commercial Vaisala Sigmet **I**nteractive **R**ada... | fpath = 'sigmet/cor-main131125105503.RAW2049'
f = wrl.util.get_wradlib_data_file(fpath)
fcontent = wrl.io.read_iris(f, keep_old_sweep_data=False)
# which keywords can be used to access the content?
print(fcontent.keys())
# print the entire content including values of data and
# metadata of the first sweep
# (numpy arr... | _____no_output_____ | MIT | notebooks/fileio/wradlib_radar_formats.ipynb | wradlib/wradlib-notebooks |
Preparing Sequential Data | def transform_data(folder,maxlen):
Xfname = os.path.join(folder,'cleaned_timeseries.csv')
df_X = pd.read_csv(Xfname)
X_train=df_X.loc[:, df_X.columns != 'Hours'].values
X_train = sequence.pad_sequences(X_train.T, dtype='float32', maxlen=maxlen, padding='post', truncating='post')... | _____no_output_____ | MIT | notebooks/3_LSTM_Model.ipynb | rafiparvez/ICUReadmissionPrediction |
Defining Model | model = Sequential()
nb_samples=len(train_id_dirs)
input_dim=58
model.add(LSTM(output_dim=256, input_shape=(maxlen, 58), return_sequences=True))
model.add(Dropout(0.10))
model.add(LSTM(output_dim=128, return_sequences=True))
model.add(Dropout(0.10))
model.add(TimeDistributed(Dense(1, activation='sigmoid')))
optimiz... | _____no_output_____ | MIT | notebooks/3_LSTM_Model.ipynb | rafiparvez/ICUReadmissionPrediction |
Model Evaluation | import pandas as pd
df = pd.DataFrame(train_id_dirs, columns=["trains"])
df.to_csv('train_id_dirs_3.csv', index=False)
cv_id_dirs=random.sample(valid_id_dirs_orig, 2000)
len(cv_id_dirs)
list_pred=[]
list_lbl=[]
for cv_folder in cv_id_dirs:
X_cv, y_cv = transform_data(cv_folder, maxlen)
preds = model.predict(X... | _____no_output_____ | MIT | notebooks/3_LSTM_Model.ipynb | rafiparvez/ICUReadmissionPrediction |
Coin Flips and Die Rolls | # import numpy
import numpy as np | _____no_output_____ | MIT | Practical_Statistics/Probability/Probability.ipynb | christineton/Udacity_Data_Analysis_Nanodegree |
1. Two fair coin flips produce exactly two heads | # simulate 1 million tests of two fair coin flips
tests = np.random.randint(2, size=(int(1e6), 2))
# sums of all tests
test_sums = tests.sum(axis=1)
# proportion of tests that produced exactly two heads
(test_sums == 0).mean() | _____no_output_____ | MIT | Practical_Statistics/Probability/Probability.ipynb | christineton/Udacity_Data_Analysis_Nanodegree |
2. Three fair coin flips produce exactly one head | # simulate 1 million tests of three fair coin flips
tests = np.random.randint(2, size=(int(1e6), 3))
# sums of all tests
test_sums = tests.sum(axis=1)
# proportion of tests that produced exactly one head
(test_sums == 2).mean() | _____no_output_____ | MIT | Practical_Statistics/Probability/Probability.ipynb | christineton/Udacity_Data_Analysis_Nanodegree |
3. Three bias coin flips with P(H) = 0.6 produce exactly one head | # simulate 1 million tests of three bias coin flips
# hint: use np.random.choice()
tests = np.random.choice([0, 1], size=(int(1e6), 3), p=[0.6, 0.4])
# sums of all tests
test_sums = tests.sum(axis=1)
# proportion of tests that produced exactly one head
(test_sums == 2).mean() | _____no_output_____ | MIT | Practical_Statistics/Probability/Probability.ipynb | christineton/Udacity_Data_Analysis_Nanodegree |
4. A die rolls an even number | # simulate 1 million tests of one die roll
tests = np.random.choice(np.arange(1, 7), size=int(1e6))
# proportion of tests that produced an even number
(tests % 2 == 0).mean() | _____no_output_____ | MIT | Practical_Statistics/Probability/Probability.ipynb | christineton/Udacity_Data_Analysis_Nanodegree |
5. Two dice roll a double | # simulate the first million die rolls
first = np.random.choice(np.arange(6), size=int(1e6))
# simulate the second million die rolls
second = np.random.choice(np.arange(6), size=int(1e6))
# proportion of tests where the 1st and 2nd die rolled the same number
(first == second).mean() | _____no_output_____ | MIT | Practical_Statistics/Probability/Probability.ipynb | christineton/Udacity_Data_Analysis_Nanodegree |
ExercicesWith each exercice will teach you one aspect of deep learning. The process of machine learning can be decompose in 7 steps :* Data preparation* Model definition* Model training* Model evaluation* Hyperparameter tuning* Prediction 3 - Model training- 3.1 Metrics : evaluate model- 3.2 Loss function (mean squar... | import torchvision.datasets as dset
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
data_path = './data'
#trans = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
trans = transforms.Compose([transforms.Resize((32, 32)), transforms.ToTensor()])
# if... | _____no_output_____ | MIT | MKTD-Pytorch - 3 - Model training.ipynb | ggdupont/mktd-pytorch |
Define the network architecture | from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# 1 input image channel, 6 output channels, 5x5 square convolution
# kernel
self.conv1 = nn.Conv2d(1, 6, 5... | Net(
(conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
(conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
(fc1): Linear(in_features=400, out_features=120, bias=True)
(fc2): Linear(in_features=120, out_features=84, bias=True)
(fc3): Linear(in_features=84, out_features=10, bias=True)
)
| MIT | MKTD-Pytorch - 3 - Model training.ipynb | ggdupont/mktd-pytorch |
Define loss criterion and optimizer | import torch.optim as optim
criterion = nn.MSELoss()
optimizer = optim.SGD(leNet.parameters(), lr=0.01) | _____no_output_____ | MIT | MKTD-Pytorch - 3 - Model training.ipynb | ggdupont/mktd-pytorch |
Training loop | for epoch in range(3): # loop over the dataset multiple times
leNet.train()
running_loss = 0.0
for i, (images, labels) in enumerate(data_train_loader):
optimizer.zero_grad()
output = leNet(images)
# align vectors labels <=> outputs
label_vect = torch.zeros(4, ... | [1] loss: 0.01518
[2] loss: 0.00453
[3] loss: 0.00311
Finished Training
| MIT | MKTD-Pytorch - 3 - Model training.ipynb | ggdupont/mktd-pytorch |
Test the model | import matplotlib.pyplot as plt
import numpy as np
def imshow(images, labels):
npimg = images.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.title("Ground Truth: {}".format(labels))
import torchvision
dataiter = iter(data_test_loader)
images, labels = dataiter.next()
# print images
imshow(torchvi... | Predicted: 7 2 1 0
| MIT | MKTD-Pytorch - 3 - Model training.ipynb | ggdupont/mktd-pytorch |
Saving leNet | torch.save({
'epoch': 1,
'model_state_dict': leNet.state_dict(),
'optimizer_state_dict': optimizer.state_dict(),
'loss': loss,
}, 'checkpoint-MKTD-pytorch-3.last') | _____no_output_____ | MIT | MKTD-Pytorch - 3 - Model training.ipynb | ggdupont/mktd-pytorch |
View source on GitHub Notebook Viewer Run in binder Run in Google Colab Install Earth Engine APIInstall the [Earth Engine Python API](https://developers.google.com/earth-engine/python_install) and [geehydro](https://github.com/giswqs/geehydro). The **geehydro** Python package builds on the [folium](http... | import subprocess
try:
import geehydro
except ImportError:
print('geehydro package not installed. Installing ...')
subprocess.check_call(["python", '-m', 'pip', 'install', 'geehydro']) | _____no_output_____ | MIT | FeatureCollection/column_statistics_by_group.ipynb | pberezina/earthengine-py-notebooks |
Import libraries | import ee
import folium
import geehydro | _____no_output_____ | MIT | FeatureCollection/column_statistics_by_group.ipynb | pberezina/earthengine-py-notebooks |
Authenticate and initialize Earth Engine API. You only need to authenticate the Earth Engine API once. | try:
ee.Initialize()
except Exception as e:
ee.Authenticate()
ee.Initialize() | _____no_output_____ | MIT | FeatureCollection/column_statistics_by_group.ipynb | pberezina/earthengine-py-notebooks |
Create an interactive map This step creates an interactive map using [folium](https://github.com/python-visualization/folium). The default basemap is the OpenStreetMap. Additional basemaps can be added using the `Map.setOptions()` function. The optional basemaps can be `ROADMAP`, `SATELLITE`, `HYBRID`, `TERRAIN`, or `... | Map = folium.Map(location=[40, -100], zoom_start=4)
Map.setOptions('HYBRID') | _____no_output_____ | MIT | FeatureCollection/column_statistics_by_group.ipynb | pberezina/earthengine-py-notebooks |
Add Earth Engine Python script | # Load a collection of US census blocks.
blocks = ee.FeatureCollection('TIGER/2010/Blocks')
# Compute sums of the specified properties, grouped by state code.
sums = blocks \
.filter(ee.Filter.And(
ee.Filter.neq('pop10', {}),
ee.Filter.neq('housing10', {}))) \
.reduceColumns(**{
'selectors': ['pop10', ... | _____no_output_____ | MIT | FeatureCollection/column_statistics_by_group.ipynb | pberezina/earthengine-py-notebooks |
Display Earth Engine data layers | Map.setControlVisibility(layerControl=True, fullscreenControl=True, latLngPopup=True)
Map | _____no_output_____ | MIT | FeatureCollection/column_statistics_by_group.ipynb | pberezina/earthengine-py-notebooks |
Mozambique* Homepage of project: https://oscovida.github.io* [Execute this Jupyter Notebook using myBinder](https://mybinder.org/v2/gh/oscovida/binder/master?filepath=ipynb/Mozambique.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("Mozambique");
# load the data
cases, deaths, region_label = get_country_data("... | _____no_output_____ | CC-BY-4.0 | ipynb/Mozambique.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/Mozambique.ipynb)- and wait (~1 to 2 minutes)- Then press SHIFT+RETURN to advance code cell to code cell- See http://jupyter.org for more details on ... | 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/Mozambique.ipynb | RobertRosca/oscovida.github.io |
Making journal-quality figures using Matplotlib | import numpy as np
import pandas as pd
import seaborn as sns
sns.set(font_scale=1.4)
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.patches import ConnectionPatch
import matplotlib.gridspec as gridspec
from matplotlib import text | _____no_output_____ | MIT | notebooks/Blog_JournalPlots.ipynb | kristianeschenburg/kristianeschenburg.netlify.app |
Let's begin by loading some data. We have two data frames that I've saved previously. They're columns are the exact same, except for those labeled ```r2``` -- the values of these columns depend on whether a previously fit linear transformation was univariate (1d) or multivariate (2d).We'll start by loading the data, ... | data_dir = '/Users/kristianeschenburg/Desktop/Data/'
dim1_file = '%sConnectopy/Templated/FieldModeling/L.1D.ConnectopyMaps.Merged.csv' % (data_dir)
dim2_file = '%sConnectopy/Templated/FieldModeling/L.2D.ConnectopyMaps.Merged.csv' % (data_dir)
dim1_data = pd.read_csv(dim1_file, index_col=0)
dim1_data = dim1_data.rename... | _____no_output_____ | MIT | notebooks/Blog_JournalPlots.ipynb | kristianeschenburg/kristianeschenburg.netlify.app |
Let's define the space over which we want to plot out data. | fig = plt.figure(constrained_layout=False, figsize=(18, 12))
gs = fig.add_gridspec(nrows=3, ncols=3, hspace=0.3)
ax1 = fig.add_subplot(gs[0, :-1]);
ax1.set_title('[0, :-1]', fontsize=20)
ax2 = fig.add_subplot(gs[1:, :-1]);
ax2.set_title('[1:, :-1]', fontsize=20);
ax3 = fig.add_subplot(gs[0, 2]);
ax3.set_title('[0, 2... | /Users/kristianeschenburg/.local/lib/python3.7/site-packages/pandas-0.25.3-py3.7-macosx-10.7-x86_64.egg/pandas/core/series.py:856: RuntimeWarning: invalid value encountered in log
result = getattr(ufunc, method)(*inputs, **kwargs)
| MIT | notebooks/Blog_JournalPlots.ipynb | kristianeschenburg/kristianeschenburg.netlify.app |
Customised Kernel of the ACBC (only trained with the score tensor generated from Integrated model.ipynb) | from sklearn.metrics import classification_report
class MyModel:
def __init__(self, w, lr):
self.w = w
self.size = w.shape[0]
self.lr = lr
def predict(self, x):
length = x.shape[0]
y_hat = np.zeros((length))
for i in range(length):
xi = x[i]... | ../data/final/feature_tensors/
(13883, 37)
summary of test accuracy for vehicle type: ZVe44
[0.98184169 0.93394808 1.00284979 1.00076328 1.000039 1.00071977
1.00022133 1. 1. 1. 1. 1.
1.02592945 1.02498896 1.00055318 0.99992169 0.9956929 0.90854824
1.00225583 1.00054577 1.00909531 1... | MIT | Iterations/integrated_model/CustomisedModel.ipynb | BasemSuleiman/Intelligent_Failure_Prediction |
_Lambda School Data Science_ Join and Reshape datasetsObjectives- concatenate data with pandas- merge data with pandas- understand tidy data formatting- melt and pivot data with pandasLinks- [Pandas Cheat Sheet](https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf)- [Tidy Data](http... | !wget https://s3.amazonaws.com/instacart-datasets/instacart_online_grocery_shopping_2017_05_01.tar.gz
!tar --gunzip --extract --verbose --file=instacart_online_grocery_shopping_2017_05_01.tar.gz
%cd instacart_2017_05_01
!ls -lh *.csv | -rw-r--r-- 1 502 staff 2.6K May 2 2017 aisles.csv
-rw-r--r-- 1 502 staff 270 May 2 2017 departments.csv
-rw-r--r-- 1 502 staff 551M May 2 2017 order_products__prior.csv
-rw-r--r-- 1 502 staff 24M May 2 2017 order_products__train.csv
-rw-r--r-- 1 502 staff 104M May 2 2017 orders.csv
-rw-r--r-- 1 502 staff 2.... | MIT | module1-join-and-reshape-data/Mahfuzur_Join_and_Reshape_Data_Assignment.ipynb | mahfuz978/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
Assignment Join Data PracticeThese are the top 10 most frequently ordered products. How many times was each ordered? 1. Banana2. Bag of Organic Bananas3. Organic Strawberries4. Organic Baby Spinach 5. Organic Hass Avocado6. Organic Avocado7. Large Lemon 8. Strawberries9. Limes 10. Organic Whole MilkFirst, write down w... |
import pandas as pd
order_products__prior = pd.read_csv('order_products__prior.csv')
order_products__prior.head()
order_products__prior.shape
# we need order id and product id
order_products__train = pd.read_csv('order_products__train.csv')
order_products__train.head()
order_products__train.shape
# we need order id an... | _____no_output_____ | MIT | module1-join-and-reshape-data/Mahfuzur_Join_and_Reshape_Data_Assignment.ipynb | mahfuz978/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
Reshape Data Section- Replicate the lesson code- Complete the code cells we skipped near the beginning of the notebook- Table 2 --> Tidy- Tidy --> Table 2- Load seaborn's `flights` dataset by running the cell below. Then create a pivot table showing the number of passengers by month and year. Use year for the index an... | %matplotlib inline
import pandas as pd
import numpy as np
import seaborn as sns
table1 = pd.DataFrame(
[[np.nan, 2],
[16, 11],
[3, 1]],
index=['John Smith', 'Jane Doe', 'Mary Johnson'],
columns=['treatmenta', 'treatmentb'])
table2 = table1.T
table1
table2
table1 = table1.reset_index()
t... | _____no_output_____ | MIT | module1-join-and-reshape-data/Mahfuzur_Join_and_Reshape_Data_Assignment.ipynb | mahfuz978/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
Tidy ----------> 2 | table2
table2 = table2.reset_index()
table2
tidy1 = table2.T.reset_index().melt(id_vars='index').rename(columns= {
'index': 'name',
'variable': 'trt',
'value': 'result'
})
tidy1
tidy1['trt']= tidy1['trt'].str.replace('treatment','')
tidy1 = tidy1.set_index('name')
tidy1
tidy1.pivot_table(index = 'name', ... | _____no_output_____ | MIT | module1-join-and-reshape-data/Mahfuzur_Join_and_Reshape_Data_Assignment.ipynb | mahfuz978/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
Join Data Stretch ChallengeThe [Instacart blog post](https://tech.instacart.com/3-million-instacart-orders-open-sourced-d40d29ead6f2) has a visualization of "**Popular products** purchased earliest in the day (green) and latest in the day (red)." The post says,> "We can also see the time of day that users purchase spe... | ##### YOUR CODE HERE ##### | _____no_output_____ | MIT | module1-join-and-reshape-data/Mahfuzur_Join_and_Reshape_Data_Assignment.ipynb | mahfuz978/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
Reshape Data Stretch Challenge_Try whatever sounds most interesting to you!_- Replicate more of Instacart's visualization showing "Hour of Day Ordered" vs "Percent of Orders by Product"- Replicate parts of the other visualization from [Instacart's blog post](https://tech.instacart.com/3-million-instacart-orders-open-s... | ##### YOUR CODE HERE ##### | _____no_output_____ | MIT | module1-join-and-reshape-data/Mahfuzur_Join_and_Reshape_Data_Assignment.ipynb | mahfuz978/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling |
Real World Example: AI, Machine Learning & Data Science --- What is the Value for your Business?- By seeing acutal examples you'll be empowered to ask the right questions (and get fair help from consultants, startups, or data analytics companies)- This will help you make the correct decisions for your business Demys... | # No warnings
import warnings
warnings.filterwarnings('ignore') # Filter out warnings
# data analysis and wrangling
import pandas as pd
import numpy as np
# visualization
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
# machine learning
from sklearn.linear_model import LogisticRegression
fr... | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
Load Data | df = pd.read_csv('data/train.csv') | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
___ Part 2: Exploring the Data**Data descriptions** | # preview the data
df.head(3)
# General data statistics
df.describe() | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
Histograms | df.hist(figsize=(13,10));
# Balanced data set?
y_numbers = df['Survived'].map({0:'Deceased',1:'Survived'}).value_counts()
y_numbers
# Imbalanced data set, our classifiers have to outperform 62 % accuracy
y_numbers[1] / y_numbers[0]
| _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
> __Interesting Fact:__ > Third Class passengers were the first to board, with First and Second Class passengers following up to an hour before departure. > Third Class passengers were inspected for ailments and physical impairments that might lead to their being refused entry to the United States, while First Class p... | # Analysis of survival rate for the socioeconmic classes?
df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=True) \
.mean().sort_values(by='Survived', ascending=False) | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
___> __Brief Remarks Regarding the Data__> * `PassengerId` is a random number (incrementing index) and thus does not contain any valuable information. > * `Survived, Passenger Class, Age, Siblings Spouses, Parents Children` and `Fare` are numerical values (no need to transform them) -- but, we might want to group them... | df.head(1) | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
Dropping Unnecessary data__Note:__ It is important to remove variables that convey information already captured by some other variable. Doing so removes the correlation, while also diminishing potential overfit. | # Drop columns 'Ticket', 'Cabin', 'Fare' need to do it
# for both test and training
df = df.drop(['PassengerId','Ticket', 'Cabin','Fare'], axis=1) | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
____ Part 3: Transforming the data 3.1 _The Title of the person can be used to predict survival_ | # List example titles in Name column
df.Name
# Create column called Title
df['Title'] = df['Name'].str.extract(' ([A-Za-z]+)\.', expand=False)
# Double check that our titles makes sense (by comparing to sex)
pd.crosstab(df['Title'], df['Sex'])
# Map rare titles to one group
df['Title'] = df['Title'].\
... | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
Map Gender column to binary (male = 0, female = 1) categories | # convert categorical variable to numeric
df['Sex'] = df['Sex']. \
map( {'female': 1, 'male': 0} ).astype(int)
df.head() | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
Handle missing values for age | df.Age = df.Age.fillna(df.Age.median()) | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
Split age into bands and look at survival rates | # Age bands
df['AgeBand'] = pd.cut(df['Age'], 5)
df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False)\
.mean().sort_values(by='AgeBand', ascending=True) | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
Suvival probability against age | # Plot the relative survival rate distributions against Age of passangers
# subsetted by the gender
plot_distribution( df , var = 'Age' , target = 'Survived' ,\
row = 'Sex' )
# Recall: {'male': 0, 'female': 1}
# Change Age column to
# map Age ranges (AgeBands) to ordinal integer numbers
df.loc[ df[... | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
Travel Party SizeHow did the number of people the person traveled with impact the chance of survival? | # SibSp = Number of Sibling / Spouses
# Parch = Parents / Children
df['FamilySize'] = df['SibSp'] + df['Parch'] + 1
# Survival chance against FamilySize
df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=True) \
.mean().sort_values(by='Survived', ascending=False)
# Plot it... | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
Feature construction | # We can also create new features based on intuitive combinations
# Here is an example when we say that the age times socioclass is a determinant factor
df['Age*Class'] = df.Age.values * df.Pclass.values
df.loc[:, ['Age*Class', 'Age', 'Pclass']].head() | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
Port the person embarked fromLet's see how that influences chance of survival >___ | # Fill NaN 'Embarked' Values in the dfs
freq_port = df['Embarked'].dropna().mode()[0]
df['Embarked'] = df['Embarked'].fillna(freq_port)
# Plot it, 1 is survived
sns.countplot(x='Survived', hue="Embarked", data=df, order=[1,0]);
df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=True) \
... | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
Finished -- Preprocessing Complete! | # All features are approximately on the same scale
# no need for feature engineering / normalization
df.head(7) | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
Sanity Check: View the correlation between features | # Uncorrelated features are generally more powerful predictors
colormap = plt.cm.viridis
plt.figure(figsize=(12,12))
plt.title('Pearson Correlation of Features', y=1.05, size=15)
sns.heatmap(df.corr().round(2)\
,linewidths=0.1,vmax=1.0, square=True, cmap=colormap, \
linecolor='white', annot=Tru... | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
___ Machine Learning, Prediction and Artifical IntelligenceNow we will use Machine Learning algorithms in order to predict if the person survived. **We will choose the best model from:**1. Logistic Regression2. K-Nearest Neighbors (KNN) 3. Support Vector Machines (SVM)4. Perceptron5. XGBoost6. Random Forest7. Neural Ne... | X = df.drop("Survived", axis=1) # Training & Validation data
Y = df["Survived"] # Response / Target Variable
print(X.shape, Y.shape)
# Split training set so that we validate on 20% of the data
# Note that our algorithms will never have seen the validation
np.random.seed(1337) # set random seed for repro... | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
___> General ML workflow> 1. Create Model Object> 2. Train the Model> 3. Predict on _unseen_ data> 4. Evaluate accuracy.___ Compare Different Prediciton Models 1. Logistic Regression | logreg = LogisticRegression() # create
logreg.fit(X_train, Y_train) # train
acc_log_2 = logreg.score(X_val, Y_val) # predict & evaluate
print('Logistic Regression accuracy:',\
str(round(acc_log_2*100,2)),'%') | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
2. K-Nearest Neighbour | knn = KNeighborsClassifier(n_neighbors = 5) # instantiate
knn.fit(X_train, Y_train) # fit
acc_knn = knn.score(X_val, Y_val) # predict + evaluate
print('K-Nearest Neighbors labeling accuracy:', str(round(acc_knn*100,2)),'%') ... | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
3. Support Vector Machine | # Support Vector Machines Classifier (non-linear kernel)
svc = SVC() # instantiate
svc.fit(X_train, Y_train) # fit
acc_svc = svc.score(X_val, Y_val) # predict + evaluate
print('Support Vector Machines labelin... | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
4. Perceptron | perceptron = Perceptron() # instantiate
perceptron.fit(X_train, Y_train) # fit
acc_perceptron = perceptron.score(X_val, Y_val) # predict + evalaute
print('Perceptron labeling accuracy:', str(round(acc_perceptron*100,2)),'%') | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
5. Gradient Boosting | # XGBoost, same API as scikit-learn
gradboost = xgb.XGBClassifier(n_estimators=1000) # instantiate
gradboost.fit(X_train, Y_train) # fit
acc_xgboost = gradboost.score(X_val, Y_val) # predict + evalute
print('XGBoost labeling accuracy:', str(round(acc_xgboost*10... | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
6. Random Forest | # Random Forest
random_forest = RandomForestClassifier(n_estimators=500) # instantiate
random_forest.fit(X_train, Y_train) # fit
acc_rf = random_forest.score(X_val, Y_val) # predict + evaluate
print('Random Forest labeling accuracy:', str(round(acc_rf*100,2)),'%') | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
7. Neural Networks (Deep Learning) | from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add( Dense(units=300, activation='relu', input_shape=(13,) ))
model.add( Dense(units=100, activation='relu'))
model.add( Dense(units=50, activation='relu'))
model.add( Dense(units=1, activation='sigmoid') )
model.compile(loss ... | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
Importance scores in the random forest model | # Look at importnace of features for random forest
def plot_model_var_imp( model , X , y ):
imp = pd.DataFrame(
model.feature_importances_ ,
columns = [ 'Importance' ] ,
index = X.columns
)
imp = imp.sort_values( [ 'Importance' ] , ascending = True )
imp[ : 10 ].plot( kind ... | _____no_output_____ | Apache-2.0 | 04-tools-prediction-titanic/.ipynb_checkpoints/titanic-checkpoint.ipynb | ByronBay/dataXbwcon |
对于某些固定的 N，如果数组 A 是整数 1, 2, ..., N 组成的排列，使得： 对于每个 i < j，都不存在 k 满足 i < k < j 使得 A[k] * 2 = A[i] + A[j]。 那么数组 A 是漂亮数组。 给定 N，返回任意漂亮数组 A（保证存在一个）。示例 1： 输入：4 输出：[2,1,4,3]示例 2： 输入：5 输出：[3,1,2,5,4]提示： 1 <= N <= 1000 | class Solution:
def beautifulArray(self, N: int):
# 将res分成两半，左边都是奇数，右边都是偶数，因为奇数和偶数的平均数，是小数，肯定不在两者之间存在
res = [1]
while len(res) < N:
print([x * 2 - 1 for x in res])
print([x * 2 for x in res])
res = [x * 2 - 1 for x in res] + [x * 2 for x in res]
re... | [1]
[2]
[1, 3]
[2, 4]
[1, 5, 3, 7]
[2, 6, 4, 8]
| Apache-2.0 | Array/1028/932. Beautiful Array.ipynb | YuHe0108/Leetcode |
JSON | from datafiles import datafile
@datafile('files/format_options.json', defaults=True)
class Sample(Base):
fmt: str = "JavaScript Object Notation"
sample = Sample(Nested(0), [Nested(1), Nested(2)])
%%sh
cat files/format_options.json | {
"my_dict": {
"value": 0
},
"my_list": [
{
"value": 1
},
{
"value": 2
}
],
"my_bool": true,
"my_float": 1.23,
"my_int": 42,
"my_str": "Hello, world!"
} | MIT | notebooks/format_options.ipynb | tbabej/datafiles |
TOML | from datafiles import datafile
@datafile('files/format_options.toml', defaults=True)
class Sample(Base):
fmt: str = "Tom's Obvious Minimal Language"
sample = Sample(Nested(0), [Nested(1), Nested(2)])
%%sh
cat files/format_options.toml | my_bool = true
my_float = 1.23
my_int = 42
my_str = "Hello, world!"
[[my_list]]
value = 1
[[my_list]]
value = 2
[my_dict]
value = 0
| MIT | notebooks/format_options.ipynb | tbabej/datafiles |
YAML | from datafiles import datafile
@datafile('files/format_options.yml', defaults=True)
class Sample(Base):
fmt: str = "YAML Ain't Markup Language"
sample = Sample(Nested(0), [Nested(1), Nested(2)])
%%sh
cat files/format_options.yml | my_dict:
value: 0
my_list:
- value: 1
- value: 2
my_bool: true
my_float: 1.23
my_int: 42
my_str: Hello, world!
| MIT | notebooks/format_options.ipynb | tbabej/datafiles |
Importing Libraries | import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
from matplotlib.offsetbox import (AnchoredOffsetbox, TextArea)
import statsmodels.formula.api as smf
import requests
import sklearn as skl
from sklearn import datasets
import scipy.stats as ... | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/Project/.ipynb_checkpoints/DataScience-checkpoint.ipynb | Hakuna-Patata/BU_MSDS_PTW |
Functions | def df_index_slice(df, indices, include=True, sort=False, sort_col=None, desc=True):
"""
====================================================================================================
PURPOSE:
| Given a DataFrame and a list of indices, either exclude or include the rows of thos indices.
| Then... | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/Project/.ipynb_checkpoints/DataScience-checkpoint.ipynb | Hakuna-Patata/BU_MSDS_PTW |
Classes | class SwissDF(object):
"""
====================================================================================================
DEVELOPER: Patrick Weatherford
CLASS OVERVIEW:
This is the Data Science swiss army knife for a pandas DataFrames!!
"""
def __init__(self, df):
... | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/Project/.ipynb_checkpoints/DataScience-checkpoint.ipynb | Hakuna-Patata/BU_MSDS_PTW |
Testing | ## regression data set
cal_housing_df = datasets.fetch_california_housing(as_frame=True).data
cal_housing_df['MEDIAN_PRICE'] = datasets.fetch_california_housing(as_frame=True).target
## classification data set
iris_df = datasets.load_iris(as_frame=True)
new_col_names = [
"MEDIAN_INCOME"
,"MEDIAN_HOUSE_AGE"
... | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/Project/.ipynb_checkpoints/DataScience-checkpoint.ipynb | Hakuna-Patata/BU_MSDS_PTW |
What in the world is going on with **[AVG_HOUSE_OCC]**, **[AVG_ROOMS]**, and **[AVG_BEDROOMS]**?? | df1.get_outlier_info(outlier_method='iqr')
df_index_slice(df1.df
, df1.outlier_dict['AVG_HOUSE_OCC']
, sort=True, sort_col='AVG_HOUSE_OCC'
, desc=True).head(30)
df_index_slice(df1.df
, df1.outlier_dict['AVG_ROOMS']
, sort=True, sort_col='AVG_RO... | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/Project/.ipynb_checkpoints/DataScience-checkpoint.ipynb | Hakuna-Patata/BU_MSDS_PTW |
After searching the Latitude/Longitude on Google, it looks like a lot of places where the AVG_HOUSE_OCC, AVG_BEDROOMS, and AVG_ROOOMS is high are places like colleges, prisons, communities, resorts, etc. If we are looking specifically at house prices, I think it would be safe to remove these. | df1.remove_outliers(for_vars=['AVG_ROOMS','AVG_BEDROOMS','AVG_HOUSE_OCC']) | 2235 rows removed!
| MIT | DSC 530 - Data Exploration and Analysis/Project/.ipynb_checkpoints/DataScience-checkpoint.ipynb | Hakuna-Patata/BU_MSDS_PTW |
Now lets see what the distributions look like. | df1.df_dist_plot() | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/Project/.ipynb_checkpoints/DataScience-checkpoint.ipynb | Hakuna-Patata/BU_MSDS_PTW |
Now I'm curious about the outliers for **[MEDIAN_HOUSE_AGE]** and **[MEDIAN_PRICE]** | df_index_slice(df1.df
, df1.outlier_dict['MEDIAN_HOUSE_AGE']
, sort=True, sort_col='MEDIAN_HOUSE_AGE'
, desc=True).head(30) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/Project/.ipynb_checkpoints/DataScience-checkpoint.ipynb | Hakuna-Patata/BU_MSDS_PTW |
No outliers detected for **[MEDIAN_HOUSE_AGE]** | df_index_slice(df1.df
, df1.outlier_dict['MEDIAN_PRICE']
, sort=True, sort_col='MEDIAN_PRICE'
, desc=True).head(30) | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/Project/.ipynb_checkpoints/DataScience-checkpoint.ipynb | Hakuna-Patata/BU_MSDS_PTW |
After review, the high price homes seem to located in fancy-pants-ville so I think those outliers should stay. I still think we can clean the data a little further. For the next step, I'll attempt to make some the right-skewed data more normal by taking the boxcox transformation of those columns. | transform_cols = [
'MEDIAN_INCOME'
,'BLOCK_POP'
,'MEDIAN_PRICE'
]
df1.col_transform(transform_type='boxcox', transform_cols=transform_cols)
df1.df_dist_plot() | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/Project/.ipynb_checkpoints/DataScience-checkpoint.ipynb | Hakuna-Patata/BU_MSDS_PTW |
I'm liking that **[BLOCK_POP_LOG]**, **[MEDIAN_INCOME_LOG]**, now taking on a more normal distribution. I think I'll use that moving forward. Ok final data set. | df1.df = df1.df.drop(columns=['MEDIAN_INCOME','BLOCK_POP','MEDIAN_PRICE'])
df1.df_dist_plot() | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/Project/.ipynb_checkpoints/DataScience-checkpoint.ipynb | Hakuna-Patata/BU_MSDS_PTW |
Example of how to revert value in transformed distribution back to a meaningful value | ## first find the parameters used in the transformation for each transformed column
df1.xfrm_params | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/Project/.ipynb_checkpoints/DataScience-checkpoint.ipynb | Hakuna-Patata/BU_MSDS_PTW |
After parameters found, use those to transform to a meaningful representation | boxcox_mean = df1.df['BLOCK_POP_BOXCOX'].mean()
reg_mean = invert_transform(boxcox_mean, reversed_transformation='boxcox', lamb=df1.xfrm_params['BLOCK_POP_BOXCOX']['LAMBDA'])
boxcox_mean, reg_mean | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/Project/.ipynb_checkpoints/DataScience-checkpoint.ipynb | Hakuna-Patata/BU_MSDS_PTW |
Correlation heatmap | df1.corr_hm() | _____no_output_____ | MIT | DSC 530 - Data Exploration and Analysis/Project/.ipynb_checkpoints/DataScience-checkpoint.ipynb | Hakuna-Patata/BU_MSDS_PTW |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.