I am plotting a shape file with Geopandas. Additionally im Adding Points of a dataframe (see picture). Now im trying to add a legend (at the right of the original plot) for the point. I dont really know how to do that!
Plot
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import geopandas as gpd
import test
variable = 'RTD_rtd'
df = test.getdataframe()
gdf = gpd.GeoDataFrame(
df, geometry=gpd.points_from_xy(df.NP_LongDegree, df.NP_LatDegree))
fp = "xxx"
map_df = gpd.read_file(fp)
ax = map_df.plot(color='white', edgecolor='black', linewidth=0.4, figsize= (10,10))
gdf.plot(column=variable, ax=ax, cmap='Reds', markersize=14.0, linewidth=2.0)
plt.show()
One Idea was to add a simple legend. I want something looking better. Maybe something similar to whats done in this tutorial: Tutorial
I followed the example that you referred to and this is the concise version. It would have been better if you could have shared a bit of your dataset 'df'. It seems that you want to have a colorbar which fig.colorbar generates.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import geopandas as gpd
import test
from shapely.geometry import Point
df = pd.read_csv('london-borough-profiles.csv', header=0)
df = df[['Area name','Population density (per hectare) 2017']]
fp = 'London_Borough_Excluding_MHW.shp'
map_df = gpd.read_file(fp)
gdf = map_df.set_index('NAME').join(df.set_index('Area name'))
variable = 'Population density (per hectare) 2017'
vmin, vmax = 120, 220
fig, ax = plt.subplots(1, figsize=(10, 6))
gdf.plot(column=variable, cmap='Blues', ax = ax, linewidth=0.8, edgecolor='0.8')
ax.axis('off')
ax.set_title('Population density (per hectare) 2017', fontdict={'fontsize': '25', 'fontweight' : '3'})
ax.annotate('Source: London Datastore, 2014',xy=(0.1, .08), xycoords='figure fraction', horizontalalignment='left', verticalalignment='top', fontsize=12, color='#555555')
sm = plt.cm.ScalarMappable(cmap='Blues', norm=plt.Normalize(vmin=vmin, vmax=vmax))
sm._A = []
cbar = fig.colorbar(sm)
You can add this into your solution and for this you have to set label for each plot
plt.legend()
Related
The codes below put dots on the specific points on the earth map.
num_samples = 1250000
indices = np.random.choice(df.index, num_samples)
df_x = df.df_longitude[indices].values
df_y = df.df_latitude[indices].values
sns.set_style('white')
fig, ax = plt.subplots(figsize=(11, 12))
ax.scatter(df_x, df_y, s=5, color='red', alpha=0.5)
ax.set_xlim([-74.10, -73.60])
ax.set_ylim([40.85, 40.90])
ax.set_title('coordinates')
Is there any way to put these dots on a map instead of this white background?
Please have a look at the picture below:
geopandas provides an API that makes this quite easy. Here is an example, where the map is zoom into the continent of Africa:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import geopandas
df = pd.DataFrame(
{'Latitude': np.random.uniform(-20,10, 100),
'Longitude': np.random.uniform(40,20, 100)})
gdf = geopandas.GeoDataFrame(
df, geometry=geopandas.points_from_xy(df.Longitude, df.Latitude))
world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
ax = world[world.continent == 'Africa'].plot(color='white', edgecolor='black')
# We can now plot our ``GeoDataFrame``.
gdf.plot(ax=ax, color='red')
plt.show()
The result:
import numpy as np
import pandas as pd
df = pd.DataFrame({"y" : np.random.rand(20)})
ax = df.iloc[:15,:].plot(ls="-", color="b")
ax2 = ax.twinx() #Create a twin Axes sharing the xaxis
df.iloc[15:,:].plot(ls="--", color="r", ax=ax)
plt.axhline(y=0.5,linestyle="--",animated=True,label="False Alaram")
plt.show()
So, first 15 are trend and last 5 are predictions.
I want different colors for trend and pred in background.
Also, how can i add text "Historic" and "Forecast" on graph.
I believe you're looking for fill_between:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame({"y" : np.random.rand(20)})
fig, ax = plt.subplots(figsize=(8,6))
df.iloc[:15,:].plot(ls="-", color="b", ax=ax)
plt.fill_between(df.iloc[:15].index.tolist(), df.iloc[:15].y.tolist(), alpha=.25, color='b')
df.iloc[15:,:].plot(ls="--", color="r", ax=ax)
plt.axhline(y=0.5,linestyle="--", animated=True, label="False Alaram")
plt.fill_between(df.iloc[15:].index.tolist(), df.iloc[15:].y.tolist(), alpha=.25, color='r')
plt.legend()
plt.show()
If I have the plot below, how can I turn the colormap/legend into a log-scale?
import geopandas as gpd
import matplotlib.pyplot as plt
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world = world[(world.pop_est>0) & (world.name!="Antarctica")]
fig, ax = plt.subplots(1, 1)
world.plot(column='pop_est', ax=ax, legend=True)
GeoPandas plots are using matplotlib, so you can use normalization of colormap provided by it. Note than I am also specifying min and max values as mins and maxs of the column I am plotting.
world.plot(column='pop_est', legend=True, norm=matplotlib.colors.LogNorm(vmin=world.pop_est.min(), vmax=world.pop_est.max()), )
You can simply plot the log of the value instead of the value itself.
import geopandas as gpd
import matplotlib.pyplot as plt
from numpy import log10
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world = world[(world.pop_est>0) & (world.name!="Antarctica")]
world['logval'] = log10(world['pop_est'])
fig, ax = plt.subplots(1, 1)
world.plot(column='logval', ax=ax, legend=True)
I have a seaborn.heatmap plotted from a DataFrame:
import seaborn as sns
import matplotlib.pyplot as plt
fig = plt.figure(facecolor='w', edgecolor='k')
sns.heatmap(collected_data_frame, annot=True, vmax=1.0, cmap='Blues', cbar=False, fmt='.4g')
I would like to create some sort of highlight for a maximum value in each column - it could be a red box around that value, or a red dot plotted next to that value, or the cell could be colored red instead of using Blues. Ideally I'm expecting something like this:
I got the highlight working for DataFrame printing in Jupyter Notebook using tips from this answer:
How can I achieve a similar thing but on a heatmap?
We've customized the heatmap examples in the official reference. The customization examples were created from the responses from this site. It's a form of adding parts to an existing graph. I added a frame around the maximum value, but this is manual.
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import seaborn as sns
sns.set()
# Load the example flights dataset and convert to long-form
flights_long = sns.load_dataset("flights")
flights = flights_long.pivot("month", "year", "passengers")
# Draw a heatmap with the numeric values in each cell
f, ax = plt.subplots(figsize=(9, 6))
ax = sns.heatmap(flights, annot=True, fmt="d", linewidths=.5, ax=ax)
ax.add_patch(Rectangle((10,6),2,2, fill=False, edgecolor='blue', lw=3))
max value:
ymax = max(flights)
ymax
1960
flights.columns.get_loc(ymax)
11
xmax = flights[ymax].idxmax()
xmax
'July'
xpos = flights.index.get_loc(xmax)
xpos
6
ax.add_patch(Rectangle((ymax,xpos),1,1, fill=False, edgecolor='blue', lw=3))
Complete solution based on the answer of #r-beginners:
Generate DataFrame:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import seaborn
arr = np.array([[0.9336719 , 0.90119269, 0.90791181, 0.3112451 , 0.56715989,
0.83339874, 0.14571595, 0.36505745, 0.89847367, 0.95317909,
0.16396293, 0.63463356],
[0.93282304, 0.90605976, 0.91276066, 0.30288519, 0.56366228,
0.83032344, 0.14633036, 0.36081791, 0.9041638 , 0.95268572,
0.16803188, 0.63459491],
[0.15215358, 0.4311569 , 0.32324376, 0.51620611, 0.69872915,
0.08811177, 0.80087247, 0.234593 , 0.47973905, 0.21688613,
0.2738223 , 0.38322856],
[0.90406056, 0.89632902, 0.92220635, 0.3022458 , 0.58843012,
0.78159595, 0.17089609, 0.33443782, 0.89997103, 0.93128579,
0.15942313, 0.62644379],
[0.93868063, 0.45617598, 0.17708323, 0.81828266, 0.72986428,
0.82543775, 0.41530088, 0.2604382 , 0.33132295, 0.94686745,
0.05607774, 0.54141198]])
columns_text = [str(num) for num in range(0,12)]
index_text = ['C1', 'C2', 'C3', 'C4', 'C5']
arr_data_frame = pd.DataFrame(arr, columns=columns_text, index=index_text)
Highlighting maximum in a column:
fig,ax = plt.subplots(figsize=(15, 3), facecolor='w', edgecolor='k')
ax = seaborn.heatmap(arr_data_frame, annot=True, vmax=1.0, vmin=0, cmap='Blues', cbar=False, fmt='.4g', ax=ax)
column_max = arr_data_frame.idxmax(axis=0)
for col, variable in enumerate(columns_text):
position = arr_data_frame.index.get_loc(column_max[variable])
ax.add_patch(Rectangle((col, position),1,1, fill=False, edgecolor='red', lw=3))
plt.savefig('max_column_heatmap.png', dpi = 500, bbox_inches='tight')
Highlighting maximum in a row:
fig,ax = plt.subplots(figsize=(15, 3), facecolor='w', edgecolor='k')
ax = seaborn.heatmap(arr_data_frame, annot=True, vmax=1.0, vmin=0, cmap='Blues', cbar=False, fmt='.4g', ax=ax)
row_max = arr_data_frame.idxmax(axis=1)
for row, index in enumerate(index_text):
position = arr_data_frame.columns.get_loc(row_max[index])
ax.add_patch(Rectangle((position, row),1,1, fill=False, edgecolor='red', lw=3))
plt.savefig('max_row_heatmap.png', dpi = 500, bbox_inches='tight')
Hello how can i make a figure with scatter subplots using pandas? Its working with plot, but not with scatter.
Here an Example
import numpy as np
import pandas as pd
matrix = np.random.rand(200,5)
df = pd.DataFrame(matrix,columns=['index','A','B','C','D'])
#single plot, working with
df.plot(
kind='scatter',
x='index',
y='A',
s= 0.5
)
# not workig
df.plot(
subplots=True,
kind='scatter',
x='index',
y=['A','B','C'],
s= 0.5
)
Error
raise ValueError(self._kind + " requires an x and y column")
ValueError: scatter requires an x and y column
Edit:
Solution to make a figure with subplots with using df.plot
(Thanks to #Fourier)
import numpy as np
import pandas as pd
matrix = np.random.rand(200,5)#random data
df = pd.DataFrame(matrix,columns=['index','A','B','C','D']) #make df
#get a list for subplots
labels = list(df.columns)
labels.remove('index')
df.plot(
layout=(-1, 5),
kind="line",
x='index',
y=labels,
subplots = True,
sharex = True,
ls="none",
marker="o")
Would this work for you:
import pandas as pd
import numpy as np
df = pd.DataFrame({"index":np.arange(5),"A":np.random.rand(5),"B":np.random.rand(5),"C":np.random.rand(5)})
df.plot(kind="line", x="index", y=["A","B","C"], subplots=True, sharex=True, ls="none", marker="o")
Output
Note: This uses a line plot with invisible lines. For a scatter, I would go and loop over it.
for column in df.columns[:-1]: #[:-1] ignores the index column for my random sample
df.plot(kind="scatter", x="index", y=column)
EDIT
In order to add custom ylabels you can do the following:
axes = df.plot(kind='line', x="index", y=["A","B","C"], subplots=True, sharex=True, ls="none", marker="o", legend=False)
ylabels = ["foo","bar","baz"]
for ax, label in zip(axes, ylabels):
ax.set_ylabel(label)