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I want to plot mileposts on a map every 100 miles along the coastline. An example is shown in the figure below:
It is easy to plot the coastlines using Cartopy, but how to determine the locations of these mileposts and show them on a map? It would be better if the coastlines between the points were colored.
You can use Shapely's interpolate method to figure out the location of the mileposts. However, the tricky part of this is getting a singlepart linestring for the coastline. I messed around with a few coastline shapefiles that I downloaded, but due to the complexity and distance, getting a nice singlepart linestring was not a simple task. Therefore, I chose to digitize my own for this example (digitize.shp).
import geopandas as gpd
import numpy as np
import matplotlib
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
# hand digitized simplified version of coastline
cl_gdf = gpd.read_file("digitize.shp")
# project data from geographic crs so we have meters for interpolation below, vs degrees
cl_gdf = cl_gdf.to_crs(9311)
# get shapely ls from gdf
coastline = cl_gdf.iloc[0].geometry
interval = 160934 # approx 100 miles in meters
interval_arr = np.arange(0, coastline.length, interval )
points = [coastline.interpolate(d) for d in interval_arr] + [coastline.boundary[1]]
# create gdf from our list of interpolated Shapely points
points_gdf = gpd.GeoDataFrame(geometry=points, crs=9311)
# transform crs to wgs84 for plotting
points_gdf = points_gdf.to_crs(4326)
# add Lat and Long cols from geometry for plotting
points_gdf['Lat'] = (points_gdf['geometry'].apply(lambda geom: np.max([coord[1] for coord in geom.coords])))
points_gdf['Long'] = (points_gdf['geometry'].apply(lambda geom: np.max([coord[0] for coord in geom.coords])))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.coastlines()
ax.set_extent([points_gdf['Long'].min() - 1,
points_gdf['Long'].max() + 1,
points_gdf['Lat'].min() - 1,
points_gdf['Lat'].max() + 1],
crs=ccrs.PlateCarree())
# add our interpolated points to the Cartopy coastline
ax.scatter(points_gdf['Long'], points_gdf['Lat'], color='red', s=10)
fig = matplotlib.pyplot.gcf()
fig.set_size_inches(18.5, 10.5)
plt.show()
EDIT:
Here is a replacement for "digitize.shp" for a full working example. You can use gpd's to_file() if you want to save cl_gdf to a shapefile.
from shapely.geometry import LineString
ls_coords = [(-84.35698841221803, 29.95854398344339),
(-84.05368623055452, 30.09279249008134),
(-83.96252983715839, 30.0613020996354),
(-83.7586709937452, 29.908822314318225),
(-83.44376708928581, 29.68673219222582),
(-83.40854757365547, 29.657727885236138),
(-83.16946921461201, 29.28191493609843),
(-82.7551219719023, 28.99684403311415),
(-82.66893774541866, 28.698514018363166),
(-82.68219685718537, 28.439961338912305),
(-82.79489930720241, 28.158205213869703),
(-82.63910474394356, 27.94937420354401),
(-82.54629096157659, 27.641099854967987),
(-82.63578996600191, 27.392491509342157),
(-82.26122005859231, 26.72290636512327),
(-81.52533935553987, 25.88095276793714),
(-80.85243943337927, 25.168275510476434),
(-80.7596256510123, 25.14175728694301),
(-80.65355275687861, 25.150044231797207),
(-80.59222936495757, 25.176562455330625),
(-80.49278602670725, 25.206395456805726),
(-80.65355275687864, 24.90143588617138),
(-80.53587813994908, 25.03236961486765),
(-80.23920551416893, 25.340643963443675),
(-80.27069590461487, 25.353903075210386),
(-80.37345402080688, 25.312468350939415),
(-80.104957007531, 25.963822216479077),
(-80.07180922811422, 26.91847826368225),
(-80.66846925761622, 28.60238545805451),
(-80.72150570468307, 28.86756769338872),
(-81.19883372828465, 29.663114399391368),
(-81.43749774008545, 30.392365546560455),
(-81.47727507538558, 31.095098470196124),
(-81.26512928711821, 31.406687596713834),
(-81.09276083415097, 31.844238285015287),
(-80.7612830399832, 32.175716079183054),
(-80.48947124876561, 32.48067564981741),
(-80.07180922811422, 32.61326676748452),
(-79.32929896917841, 33.07070612343603),
(-79.20996696327803, 33.22981546463656),
(-78.9779325073606, 33.61432970587117),
(-78.55364093082585, 33.8331050500219),
(-77.93046267779044, 33.919289276505516),
(-77.71831688952308, 34.28391485009006),
(-77.45976421007221, 34.469542414824005),
(-75.76259790393324, 35.20542311787645),
(-75.76259790393324, 36.21974516802982),
(-75.84215257453349, 36.58437074161438),
(-76.17031559075959, 36.939051981373886),
(-76.29959193048501, 36.96722759387815),
(-76.2946197635725, 37.12136476816616),
(-76.41726654741457, 37.16942904832049),
(-76.50179338492735, 37.23738199612488),
(-76.3377118768143, 37.5108511763133),
(-76.72885567393226, 37.79923685723926),
(-76.76200345334904, 37.878791527839525),
(-76.31119365328087, 37.68653440722222),
(-76.23163898268061, 37.89205063960623),
(-76.35428576652268, 37.951716642556434),
(-76.38411876799778, 37.95668880946896),
(-76.50676555183985, 38.02298436830251),
(-76.52665421948991, 38.05613214771928),
(-76.6028941121485, 38.10253903890277),
(-76.60952366803185, 38.12905726243619),
(-76.85978940262852, 38.16717720876549),
(-76.90785368278284, 38.19203804332807),
(-76.94265885117046, 38.2102693220073),
(-77.01724135485821, 38.313027438199306),
(-77.01724135485821, 38.31799960511182),
(-77.26916447842571, 38.3428604396744),
(-77.30396964681333, 38.38263777497453),
(-77.29568270195914, 38.52517322646668),
(-77.23270192106726, 38.60804267500862),
(-77.21612803135888, 38.63953306545456),
(-77.17966547400042, 38.613014841921135),
(-76.96254751882053, 38.4108133874788),
(-76.81338251144503, 38.27987965878253),
(-76.41063699153119, 38.311370049228465),
(-76.53825594228582, 38.710800791200626),
(-76.53494116434413, 39.2047027045106),
(-76.0808165863343, 39.532865720736694),
(-76.02446536132577, 39.370441601594486),
(-76.06755747456758, 39.254424373635764),
(-76.14048258928449, 39.101944588318595),
(-76.1835747025263, 38.956094358884776),
(-76.196833814293, 38.88316924416787),
(-76.18688948046797, 38.76383723826747),
(-76.17031559075959, 38.64782001030875),
(-76.094075698101, 38.51854367058332),
(-75.98468802602564, 38.33291610584937),
(-75.89850379954201, 38.24341710142407),
(-75.84546735247517, 38.190380654357234),
(-75.77917179364162, 38.10419642787361),
(-75.69961712304135, 38.011382645506636),
(-75.65652500979955, 37.95171664255644),
(-75.66978412156625, 37.91525408519799),
(-75.92502202307544, 37.54068417778841),
(-75.76591268187491, 37.481018174838205),
(-75.67972845539128, 37.46112950718814),
(-75.62337723038277, 37.48433295277989),
(-75.58359989508264, 37.59703540279693),
(-75.2189743214981, 38.051159980806766),
(-75.05655020235588, 38.37600821909118),
(-75.03666153470581, 38.44893333380809),
(-75.39465755240701, 39.1450367015604),
(-75.50404522448237, 39.39033026924455),
(-75.54713733772417, 39.536180498678355),
(-75.5438225597825, 39.60413344648275),
(-75.50901739139488, 39.57595783397849),
(-75.51896172521991, 39.48977360749487),
(-75.50073044654069, 39.45331105013641),
(-75.45100877741551, 39.41684849277796),
(-75.3996297193195, 39.38701549130286),
(-75.33333416048596, 39.34889554497357),
(-75.2753255465066, 39.31409037658595),
(-75.17588220825627, 39.27431304128582),
(-75.07146670309342, 39.23453570598569),
(-74.95710686410554, 39.19144359274388),
(-74.8957834721845, 39.169897536122974),
(-74.53778745448334, 39.270998263344154),
(-74.29249388679919, 39.46325538396146),
(-74.10023676618188, 39.89417651637956),
(-74.02731165146497, 40.046656301696736),
(-74.13338454559866, 40.32509764879766),
(-74.23945743973235, 40.4974661017649),
(-74.07371854264846, 40.550502548831744),
(-73.80853630731424, 40.56376166059845),
(-73.60302007493023, 40.550502548831744),
(-73.4439107337297, 40.61016855178194),
(-73.39750384254621, 40.70298233414891),
(-73.41739251019628, 40.88198034299951),
(-73.42070728813798, 40.935016790066356),
(-73.62953829846366, 40.8985542327079),
(-73.77207374995581, 40.83225867387435),
(-73.7588146381891, 40.931702012124674),
(-73.53672451609668, 41.031145350375006),
(-73.15552505280375, 41.1504773562754),
(-72.96658271012812, 41.17368080186715),
(-71.70033753640726, 41.35930836660109),
(-71.04401150395508, 41.50515859603491),
(-70.70590415390394, 41.65100882546872),
(-70.68601548625388, 41.670897493118794),
(-70.67938593037053, 41.51178815191826),
(-70.61309037153697, 41.53830637545168),
(-70.5070174774033, 41.76371127548577),
(-70.54016525682006, 41.86978416961945),
(-70.6528677068371, 42.00237528728656),
(-70.77882926862085, 42.247668854970726),
(-70.93130905393804, 42.41340775205461),
(-70.91142038628796, 42.5924057609052),
(-70.86501349510448, 42.65870131973875),
(-70.54679481270341, 43.32165690807428),
(-70.19542835088558, 43.63987559047534),
(-69.74461855081742, 43.81887359932593),
(-69.53910231843341, 43.885169158159485),
(-68.96233095658148, 44.289572067044155),
(-68.81648072714766, 44.44868140824468),
(-68.41207781826299, 44.48845874354482),
(-67.91486112701133, 44.42216318471126),
(-67.61653111226035, 44.52823607884495),
(-67.09942575335863, 44.70723408769554),
(-67.02650063864172, 44.760270534762384)]
ls = LineString(ls_coords)
cl_gdf = gpd.GeoDataFrame(geometry=[ls], crs=4326)
I have a question for those who have some experience with timeseries forecasting.
I have been experiment with this field for few weeks and i was trying to forecast some timeseries with both ARIMA and LSTM models to compare the results.
Basically i did plot this graph Figure 1 that has 4 plots :
Top left : ARIMA training data points and fitted model points.
Top right : ARIMA test and forecast points.
Bottom Left : LSTM training data and fitted data (i could not really find fitted point for LSTM so i just forecasted the training data but you can just ignore that part).
Bottom right : Test and forecast data for the LSTM model.
This graph was acceptable and also i did compute the RMSE and MSE and LSTM gave lower error which agrees with most literature online that states the superiority of LSTM over ARIMA models.
However after i did plot the loss and validation loss of the LSTM model to have more insights, i noticed that the validation_loss is following a wierd flectuating pattern Figure 2.
I can explain this as follow : the time series has a lot of outliers or abnormal behaviour, so splitting it to train/validation/test would mean validation cannot be really a good metric to show how good the model can learn.
But since all research papers never show this graph and explain this problem, i don't have a solid argument to defende this idea.
what do you guys think?
Thank you in advance
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.stattools import adfuller
from statsmodels.graphics.tsaplots import plot_acf,plot_pacf
import statsmodels.api as sm
from statsmodels.tsa.arima.model import ARIMA
from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_percentage_error,mean_absolute_percentage_error
from statsmodels.tsa.seasonal import STL
import numpy as np
from pandas import Series, DataFrame
from scipy import stats
from statsmodels.tsa.stattools import adfuller
import statsmodels
from statsmodels.tsa.seasonal import seasonal_decompose
from pandas.plotting import register_matplotlib_converters
import pmdarima as pm
register_matplotlib_converters()
import warnings
import time
from numpy import array
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense
from numpy import array
import keras_tuner as kt
import tensorflow as tf
print(tf.__version__)
from numpy import array
from tensorflow import keras
import keras_tuner as kt
from sklearn.preprocessing import MinMaxScaler
from keras.layers import Bidirectional
from keras.models import Sequential
from keras.preprocessing.sequence import TimeseriesGenerator
from keras.layers import Bidirectional
from tensorflow.keras import initializers
import random as rn
np.random.seed(123)
rn.seed(123)
tf.random.set_seed(123)
tf.keras.utils.set_random_seed(123)
keras.utils.set_random_seed(123)
warnings.filterwarnings('ignore')
df3 = pd.read_csv('favorita_train.csv')
## 1 - Get TS and do STL
print("TS lenbgth : "+str(len(df3)))
results = seasonal_decompose(df3['unit_sales'],period=30)
results.plot();
train_all = df3.iloc[:int(len(df3)*0.8)]
train = df3.iloc[:int(len(df3)*0.6)]
val = df3.iloc[int(len(df3)*0.6):int(len(df3)*0.8)]
test = df3.iloc[int(len(df3)*0.8):]
scaler = MinMaxScaler()
scaler.fit(train_all)
scaled_all = scaler.transform(df3)
scaled_train = scaler.transform(train)
scaled_train_all = scaler.transform(train_all)
scaled_val = scaler.transform(val)
scaled_test = scaler.transform(test)
# We do the same thing, but now instead for 12 months
n_features = 1
n_input =5
train_generator_all = TimeseriesGenerator(scaled_train_all, scaled_train_all, length=n_input, batch_size=1,shuffle=True)
train_generator = TimeseriesGenerator(scaled_train, scaled_train, length=n_input, batch_size=1,shuffle=True)
val_generator = TimeseriesGenerator(scaled_val, scaled_val, length=n_input, batch_size=1,shuffle=True)
adfPValue = adfuller(scaled_all)
adfPValue=adfPValue[1]
adi = len(scaled_all)/((scaled_all != 0).sum())
sd=scaled_all.std()
mean=scaled_all.mean()
cv2 = np.square(sd/mean)
print("CV2 (describe magnitude of demande variability <0.5 is good) :"+str(cv2))
print("SD (-2,2 is good | mean data variance is low) :"+str(sd))
print("ADI (1.3 or smaller means smooth ts) :"+str(adi))
print("Stationarity test (stationary if <0.05) :"+str(adfPValue))
def model_builder(hp):
model = keras.Sequential()
hp_units = hp.Int('units', min_value=1, max_value=50, step=1)
hp_layers = hp.Int('layers', min_value=1, max_value=3, step=1)
if hp_layers==1 :
model.add(Bidirectional(LSTM(hp_units,activation='relu'), input_shape=(n_input, n_features)))
elif hp_layers==2:
model.add(Bidirectional(LSTM(hp_units, activation='relu', return_sequences=True), input_shape=(n_input, n_features)))
model.add(Bidirectional(LSTM(hp_units, activation='relu')))
else:
model.add(Bidirectional(LSTM(hp_units, activation='relu', return_sequences=True), input_shape=(n_input, n_features)))
for i in range(hp_layers-2):
model.add(Bidirectional(LSTM(hp_units, activation='relu', return_sequences=True)))
model.add(Bidirectional(LSTM(hp_units, activation='relu')))
model.add(Dense(1))
hp_learning_rate = hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])
model.compile(optimizer=keras.optimizers.Adam(learning_rate=hp_learning_rate), loss='mse',metrics=['accuracy'])
return model
tuner = kt.Hyperband(model_builder,
objective='val_loss',
max_epochs=300,
factor=3,
directory='499',
project_name='949',
seed=123)
stop_early = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=30)
tuner.search(train_generator, epochs=300, validation_data=val_generator, shuffle=True, callbacks=[stop_early], batch_size=len(train_generator))
best_hps=tuner.get_best_hyperparameters(num_trials=1)[0]
print(best_hps.get('units'))
print(best_hps.get('layers'))
print(best_hps.get('window'))
print(best_hps.get('learning_rate'))
best_hps=tuner.get_best_hyperparameters(num_trials=1)[0]
model = tuner.hypermodel.build(best_hps)
history = model.fit(img_train, label_train, epochs=50, validation_split=0.2)
val_acc_per_epoch = history.history['val_accuracy']
best_epoch = val_acc_per_epoch.index(max(val_acc_per_epoch)) + 1
print('Best epoch: %d' % (best_epoch,))
I have a multipolygon geometry which consists of a list of polygon geometries (3 in this example). I want to create a geodataframe and assign it to just one row. However, when I do the following it creates multiple rows.
Any help would be appreciated !
>>> from shapely.geometry import shape
>>> geo={'type': 'MultiPolygon', 'coordinates': [[[[-6.89361451404645, 42.3483702456613], [-6.88669573921854, 42.3512517221675], [-6.87580712586245, 42.3552054810059], [-6.86866686394711, 42.3573490202175], [-6.86634649451855, 42.3578029961007], [-6.86485929944272, 42.3578451095022], [-6.86269565884165, 42.3567199999001], [-6.84977005165036, 42.3574553272632], [-6.85736376939718, 42.3584219957405], [-6.86260312778948, 42.3586101962362], [-6.86540904591301, 42.3592523372132], [-6.86769848173221, 42.3602680395218], [-6.86944163436685, 42.3613226434774], [-6.875639521488, 42.3661014013158], [-6.87837881937712, 42.3659090495874], [-6.88128451646765, 42.3660684347702], [-6.89613000001251, 42.3681444544187], [-6.90629355446821, 42.3611397165696], [-6.91134851142469, 42.3579245448312], [-6.91590536445934, 42.3556212772542], [-6.92234623065452, 42.352847993855], [-6.92358612842103, 42.3477036195428], [-6.9224104785545, 42.3439383624458], [-6.91770201332808, 42.3426902193944], [-6.90512650312747, 42.343936876722], [-6.9031378495764, 42.3439369775367], [-6.90191893841204, 42.3435986892315], [-6.89791212110307, 42.3461065492655], [-6.89361451404645, 42.3483702456613]]], [[[-6.89906785460217, 42.4682161832092], [-6.8985645069621, 42.470555820794], [-6.89858965513706, 42.4739212739318], [-6.89896550934163, 42.4765974195716], [-6.89972491760736, 42.4787657510451], [-6.9009810281459, 42.4802088026149], [-6.90230301564329, 42.4804035982091], [-6.90426571956671, 42.4800515116974], [-6.90827881089143, 42.4788005356882], [-6.91660939687048, 42.4756837812393], [-6.92382490186326, 42.474250220658], [-6.9265398868427, 42.4731320663596], [-6.927085378973, 42.4726705844922], [-6.92751384817931, 42.4720387429714], [-6.92793713400628, 42.4707715563402], [-6.92809711825426, 42.469121060489], [-6.92777864404758, 42.4655184358035], [-6.9265916168156, 42.460554962602], [-6.92509229324126, 42.4566502755129], [-6.92312921129026, 42.4529560798592], [-6.91994977756089, 42.4483393479285], [-6.91714736214994, 42.445084760463], [-6.91523950238876, 42.4432753489897], [-6.91432193977259, 42.4426295412105], [-6.91346078340642, 42.4422568680072], [-6.91227481057592, 42.4422099855322], [-6.91121575180791, 42.4427776558529], [-6.91058022044174, 42.4434975209533], [-6.91000109532557, 42.4444905206296], [-6.90674756055746, 42.4519678712067], [-6.90334259105767, 42.4586766049039], [-6.90013178996497, 42.4652742523013], [-6.89906785460217, 42.4682161832092]]], [[[-6.81153692785486, 42.38608709317], [-6.81255364283188, 42.389076878809], [-6.81428465162502, 42.3928882452291], [-6.81468909293668, 42.3942970262505], [-6.81502856739456, 42.3965697891244], [-6.8150767586934, 42.3981913586016], [-6.81488185813205, 42.4007833041592], [-6.81391983219631, 42.405510635491], [-6.81354770683345, 42.4084498797579], [-6.81360710668831, 42.4124126081536], [-6.81406833183345, 42.4154174564801], [-6.81488670719631, 42.4184504208322], [-6.81700648277012, 42.4245123095467], [-6.81829511926674, 42.4294140920437], [-6.81898116245038, 42.4332326515733], [-6.8198464186291, 42.4413041712813], [-6.82043986166016, 42.4449014594344], [-6.82104066966606, 42.4459522550983], [-6.82188536934636, 42.4467141034876], [-6.82339631054119, 42.4475679287968], [-6.82520716870599, 42.4482527587704], [-6.82670080610246, 42.4485898913254], [-6.8278429373007, 42.4485693985685], [-6.83292590445095, 42.4467192778025], [-6.84029070701108, 42.4443130224279], [-6.84419676594343, 42.4433610436994], [-6.84792497293665, 42.442772924522], [-6.85233512738876, 42.4425494530441], [-6.85882703332382, 42.4427959126746], [-6.86207019904241, 42.4431158543985], [-6.86826707567392, 42.4431984565626], [-6.86971582822253, 42.4429462314883], [-6.87071979951853, 42.4423075911722], [-6.87138234339051, 42.4415737757853], [-6.87343079514828, 42.4381019289848], [-6.87540506959181, 42.4356805263046], [-6.87730718261172, 42.4339309548587], [-6.87994811762039, 42.4321507482297], [-6.88482749307854, 42.4293696834051], [-6.89288504801627, 42.4252654154878], [-6.90380506518841, 42.4199717157414], [-6.91512371615772, 42.414746875], [-6.90892681128518, 42.4134221875], [-6.90518184926205, 42.4129543241767], [-6.89684841190103, 42.4114517049355], [-6.89382995952878, 42.4112506910577], [-6.89044820655216, 42.4118417747366], [-6.88899405368913, 42.4113504325048], [-6.88535290642418, 42.4093367301526], [-6.87968569673017, 42.4057680005089], [-6.87414202824488, 42.4020377762816], [-6.87059213937194, 42.3993812288458], [-6.86765946068107, 42.3968980089979], [-6.86534399217227, 42.3945881167378], [-6.86414324104532, 42.3931444816688], [-6.86243928318094, 42.3904912241775], [-6.86143446636381, 42.3892932385407], [-6.86020235510347, 42.3881839241729], [-6.85874294939991, 42.387163281074], [-6.85612766376356, 42.385798575055], [-6.85410002195198, 42.3849996101285], [-6.84390772477599, 42.3822068637876], [-6.83932508071557, 42.3805370409279], [-6.83745999207373, 42.3794742338664], [-6.83322271926195, 42.3756476996947], [-6.83216467694021, 42.3748753255289], [-6.82967109419671, 42.373518847268], [-6.82611729148701, 42.3719774181725], [-6.8235117854645, 42.3715185151684], [-6.82153020724814, 42.3716568375518], [-6.81952510157509, 42.3722087310524], [-6.81681502907838, 42.3735879219019], [-6.81433436043164, 42.3754585172813], [-6.81253514636759, 42.377414128838], [-6.81141738688623, 42.3794547565721], [-6.81098108198756, 42.3815804004834], [-6.811094251368, 42.3837911369289], [-6.81153692785486, 42.38608709317]]]]}
>>> geo_shape=shape(geo_shape)
>>> import geopandas as gpd
>>> df = gpd.GeoDataFrame({'id':1,'geometry':geo_shape})
>>> df
id geometry
0 1 POLYGON ((-6.89361 42.34837, -6.88670 42.35125...
1 1 POLYGON ((-6.89907 42.46822, -6.89856 42.47056...
2 1 POLYGON ((-6.81154 42.38609, -6.81255 42.38908...
I just found the solution by doing the following. Hope it helps !
>>> df = gpd.GeoDataFrame({'id':[1],'geometry':[geo_shape]})
>>> df
id geometry
0 1 MULTIPOLYGON (((-6.89361 42.34837, -6.88670 42...
When doing df = gpd.GeoDataFrame(df1, crs = 'EPSG:4326', geometry = geopandas.points_from_xy(df1.longitude,df1.latitude)) I get "name 'pygeos' is not defined", yet I have installed pygeos in the directory where I dev and
python3.9/site-packages/geopandas/_vectorized.py in points_from_xy(x, y, z)
247
248 if compat.USE_PYGEOS:
--> 249 return pygeos.points(x, y, z)
250 else:
251 out = _points_from_xy(x, y, z)
anf import pygeos is in the script. Is there a specific way to well install pygeos in order to avoid such error ? Thanks
USE_PYGEOS=1
import pyproj
import shapely
import pandas as pd
pd.options.display.max_rows = 100
import geopandas as gpd
import numpy as np
import sklearn
import matplotlib.pyplot as plt
gpd.show_versions()
print(gpd.options.use_pygeos)
location_df = pd.read_csv("location_01-03_01-04.csv", sep = ";")
import rtree
import pygeos
gpd.options.use_pygeos = True
#Point is (longitude, latitude)
# Function making geopandas points of latitude, longitude
location_geo = gpd.GeoDataFrame(location_df, crs = 'EPSG:4326', geometry = gpd.points_from_xy(location_df.longitude, location_df.latitude))
departments_df = gpd.read_file("departements.geojson", sep = ";")
print(departments_df)
import time
start = time.time()
print("hello")
import geopandas
import rtree
# Function to check wether a department contains a position - returns the department of the position. NaN values are probably in another country
dept_points = geopandas.sjoin(location_geo, departments_df)
end = time.time()
print(end-start, ' s')
print(dept_points)
Somehow this did it for me.
It was about setting the constant and importing packages in a specific order.
I want to visualize category count by seaborn catplot but one of the hue are not important and don't need to include the visualization.
How can I select specific Hues at catplot to visualize without changing or removing any value from the column ?
You could remove the rows with that value from the dataframe. If the column is Categorical you might also need to change the categories as the legend will still contain all the categories.
Here is an example:
import seaborn as sns
import pandas as pd
tips = sns.load_dataset('tips')
tips['day'].dtype # CategoricalDtype(categories=['Thur', 'Fri', 'Sat', 'Sun'], ordered=False)
# create a subset, a copy is needed to be able to change the categorical column
tips_weekend = tips[tips['day'].isin(['Sat', 'Sun'])].copy()
tips_weekend['day'].dtype # CategoricalDtype(categories=['Thur', 'Fri', 'Sat', 'Sun'], ordered=False)
tips_weekend['day'] = pd.Categorical(tips_weekend['day'], ['Sat', 'Sun'])
tips_weekend['day'].dtype # CategoricalDtype(categories=['Sat', 'Sun'], ordered=False)
sns.catplot(data=tips_weekend, x='smoker', y='tip', hue='day')
For the follow-up question, a histplot with multiple='fill' can show the percentage distribution:
import seaborn as sns
import pandas as pd
from matplotlib.ticker import PercentFormatter
tips = sns.load_dataset('tips')
tips_weekend = tips.copy()
tips_weekend['day'] = tips_weekend['day'].apply(lambda x: x if x in ['Sat', 'Sun'] else 'other')
# fix a new order
tips_weekend['day'] = pd.Categorical(tips_weekend['day'], ['other', 'Sat', 'Sun'])
ax = sns.histplot(data=tips_weekend, x='smoker', hue='day', stat='count', multiple='fill',
palette=['none', 'turquoise', 'crimson'])
# remove the first label ('other') in the legend
ax.legend(handles=ax.legend_.legendHandles[1:], labels=['Sat', 'Sun'], title='day')
ax.yaxis.set_major_formatter(PercentFormatter(1))
# add percentages
for bar_group in ax.containers[:-1]:
ax.bar_label(bar_group, label_type='center', labels=[f'{bar.get_height() * 100:.1f} %' for bar in bar_group])