How to create multipolygon shapely geometry in one row of geopandas dataframe? - geopandas

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...

Related

Plot milepost along coastline in python

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)

Create a column to count the number of cities in a country based on coordinates

I have a dataframe with a columns for Country and Coordinates (country_gdf).
I have a seperate dataframe with a columns for City, Latitude, and Longitude (cities_gpd).
I want to add a new column to the Country dataframe for a count of the number of cities within a country. But, I'm not sure of how to work with coordinates
My sample data is as follows;
import pandas as pd
import geopandas as gpd
from shapely import wkt
# Sample Country dataframe
country = pd.DataFrame(
{'Country': ['Argentina', 'Brazil', 'Chile'],
'Coordinates': ['POINT(-58.66000 -34.58000)', 'POINT(-47.91000 -15.78000)',
'POINT(-70.66000 -33.45000)']})
country['Coordinates'] = gpd.GeoSeries.from_wkt(country['Coordinates'])
country_gdf = gpd.GeoDataFrame(country, geometry='Coordinates')
# Sample Cities Dataframe
cities = pd.DataFrame(
{'City': ['Buenos Aires', 'Brasilia', 'Santiago'],
'Latitude': [-38.4161, -14.2350, -33.4489],
'Longitude': [-63.6167, -51.9253, -70.6693]})
cities_gpd = gpd.GeoDataFrame(cities,
geometry=gpd.points_from_xy(cities.Longitude,
cities.Latitude))
I have tried
from shapely.geometry import Point
from shapely.geometry.polygon import Polygon
polygon= country_gdf['Coordinates']
point = cities_gpd['geometry']
print(polygon.contains(point))
but the output is wrong

You need polygon boundaries for countries to check containment.
You can use the data provided with geopandas.
import pandas as pd
import geopandas as gpd
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
cities = pd.DataFrame(
{'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'São Paulo'],
'Latitude': [-38.4161, -14.2350, -33.4489, -23.55],
'Longitude': [-63.6167, -51.9253, -70.6693, -46.633333]})
cities_gpd = gpd.GeoDataFrame(cities,
geometry=gpd.points_from_xy(cities.Longitude,
cities.Latitude),
crs=4326)
countries_gpd = world.loc[world['name'].isin(['Argentina', 'Brazil', 'Chile'])]
countries_gpd.set_index('name', inplace=True)
You get the following geodataframe
pop_est continent iso_a3 gdp_md_est geometry
name
Argentina 44293293 South America ARG 879400.0 MULTIPOLYGON (((-68.63401 -52.63637, -68.25000...
Chile 17789267 South America CHL 436100.0 MULTIPOLYGON (((-68.63401 -52.63637, -68.63335...
Brazil 207353391 South America BRA 3081000.0 POLYGON ((-53.37366 -33.76838, -53.65054 -33.2...
Now you can say
countries_gpd['geometry'].contains(
cities_gpd.set_index('City')
.filter(like='Buenos Aires',axis=0)['geometry'].item())
to get
name
Argentina True
Chile False
Brazil False
Note cities_gpd needs a crs specified explicitly to avoid a warning about CRS mismatch.
Now to get a count of the cities you can go on with
countries_gpd.apply(lambda x:
cities_gpd['geometry'].within(x['geometry']).sum(), axis=1)
This would return
name
Argentina 1
Chile 1
Brazil 2
dtype: int64
meaning each country has one city.

exclude one of the hue from seaborn catplot visualization

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])

Dimensionality problem in creation of patches

I have a binary image of 650x650 size. I want to create patches of 50x50. This means that I need 169 patches. I want to examine if there is in every patch is at least "ONE" element.
I need also the result to be pairs of every patch.
Here there is an example of 2d:
2d example
So far so good.When I implement the view_as_blocks function from skimage.util.shape it returns a list of (13,13,50,50).
The way i search for "ONES" will be numpy.where but i am lost in dimensions...
here is my code:
def distinguish_patches_for_label(image_path):
im1=cv2.imread(image_path)
im2 = cv2.cvtColor(im1, cv2.COLOR_BGR2GRAY)
im3=(im2>100).astype(np.uint8)
im4 = cv2.resize(im3, (650,650))
patches=view_as_blocks(im4, block_shape=(50,50))
for i in range(patches.shape[0]):
for j in range(patches.shape[0]):
indexes_normal=list(zip(*np.where(patches[i,j,:,:]== 0)))
for i in range(patches.shape[0]):
for j in range(patches.shape[0]):
indexes_XDs=list(zip(*np.where(patches[i,j,:,:]== 1)))
list1=[]
list2=[]
for i in range(len(indexes_normal)):
list1.append(indexes_normal[i][0])
list2.append(indexes_normal[i][1])
zipped_indexes_normal=list(zip(list1,list2))
list3=[]
list4=[]
for i in range(len(indexes_XDs)):
list3.append(indexes_XDs[i][0])
list4.append(indexes_XDs[i][1])
zipped_indexes_XDs=list(zip(list3,list4))
return zipped_indexes_normal,zipped_indexes_XDs

I'm not exactly sure what you are trying to get as your output, but the below will find the number of 1s in each patch:
import numpy as np
from skimage import io, util
def count_ones_in_patches(image_path):
image = io.imread(image_path)
patches = util.view_as_blocks(image, (50, 50))
ones_per_patch = np.sum(patches, axis=(2, 3))
return ones_per_patch
Using the numpy axis= keyword argument is a very good idea in general. =)

pandas series sort_index() not working with kind='mergesort'

I needed a stable index sorting for DataFrames, when I had this problem:
In cases where a DataFrame becomes a Series (when only a single column matches the selection), the kind argument returns an error. See example:
import pandas as pd
df_a = pd.Series(range(10))
df_b = pd.Series(range(100, 110))
df = pd.concat([df_a, df_b])
df.sort_index(kind='mergesort')
with the following error:
----> 6 df.sort_index(kind='mergesort')
TypeError: sort_index() got an unexpected keyword argument 'kind'
If DataFrames (more then one column is selected), mergesort works ok.
EDIT:
When selecting a single column from a DataFrame for example:
import pandas as pd
import numpy as np
df_a = pd.DataFrame(np.array(range(25)).reshape(5,5))
df_b = pd.DataFrame(np.array(range(100, 125)).reshape(5,5))
df = pd.concat([df_a, df_b])
the following returns an error:
df[0].sort_index(kind='mergesort')
...since the selection is casted to a pandas Series, and as pointed out the pandas.Series.sort_index documentation contains a bug.
However,
df[[0]].sort_index(kind='mergesort')
works alright, since its type continues to be a DataFrame.

pandas.Series.sort_index() has no kind parameter.
here is the definition of this function for Pandas 0.18.1 (file: ./pandas/core/series.py):
# line 1729
#Appender(generic._shared_docs['sort_index'] % _shared_doc_kwargs)
def sort_index(self, axis=0, level=None, ascending=True, inplace=False,
sort_remaining=True):
axis = self._get_axis_number(axis)
index = self.index
if level is not None:
new_index, indexer = index.sortlevel(level, ascending=ascending,
sort_remaining=sort_remaining)
elif isinstance(index, MultiIndex):
from pandas.core.groupby import _lexsort_indexer
indexer = _lexsort_indexer(index.labels, orders=ascending)
indexer = com._ensure_platform_int(indexer)
new_index = index.take(indexer)
else:
new_index, indexer = index.sort_values(return_indexer=True,
ascending=ascending)
new_values = self._values.take(indexer)
result = self._constructor(new_values, index=new_index)
if inplace:
self._update_inplace(result)
else:
return result.__finalize__(self)
file ./pandas/core/generic.py, line 39
_shared_doc_kwargs = dict(axes='keywords for axes', klass='NDFrame',
axes_single_arg='int or labels for object',
args_transpose='axes to permute (int or label for'
' object)')
So most probably it's a bug in the pandas documentation...
Your df is Series, it's not a data frame

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