I am trying to format my colorbar such the numbers are formatted with commas. Any help would be greatly appreciated
import numpy as np
import matplotlib.pyplot as plt
plt.matshow(np.array(([30000,8000],[12000,25000])))
plt.colorbar()
You can create and specify a custom formatter:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter
comma_fmt = FuncFormatter(lambda x, p: format(int(x), ','))
plt.matshow(np.array(([30000,8000],[12000,25000])))
plt.colorbar(format=comma_fmt)
plt.show()
Related
I'm trying to plot netcdf raster values of snowfall data in a text format overlaying what I currently have (mentioned further below). Example, something like this below:
Example
This is all the relevant code I have so far. I excluded the non relevant code. I tried plt.text and it gave me "ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
What I have plotted so far
import numpy
from datetime import datetime
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.mpl.ticker as cticker
import matplotlib.pyplot as plt
from matplotlib import ticker, patheffects
from metpy.units import units
import numpy as np
import numpy.ma as ma
from scipy.ndimage import gaussian_filter, maximum_filter, minimum_filter
import xarray as xr
from metpy.plots import USCOUNTIES
from gradient import Gradient
import pandas as pd
import matplotlib.colors as col
#Open NOAA Snowfall dataset
ds = xr.open_dataset('sfav2_CONUS_2021093012_to_2022042512.nc')
ds
lat = ds.lat
lon = ds.lon
#converts snowfall data to inches
snowdata = ds['Data'] * 39
plt.text(lon, lat, snowdata, transform=datacrs)
As far as I know there isn't a vectorized way of plotting text (plt.text or plt.annotated). So you'll have to loop over the arrays and plot each point.
import matplotlib.pyplot as plt
import matplotlib.patheffects as PathEffects
import cartopy.crs as ccrs
import numpy as np
data = np.random.rand(18, 9)
lons, lats = np.mgrid[-17:18:2, 8:-9:-2]
lons = lons * 10
lats = lats * 10
fig, ax = plt.subplots(figsize=(10, 5), dpi=86, facecolor="w", subplot_kw=dict(projection=ccrs.EqualEarth()))
ax.pcolormesh(lons, lats, data, cmap="coolwarm", alpha=.2, transform=ccrs.PlateCarree())
ax.coastlines()
for val, lat, lon in zip(data.flat, lats.flat, lons.flat):
ax.text(
lon, lat, f"{val:1.1f}", ha="center", va="center", transform=ccrs.PlateCarree(),
path_effects=[PathEffects.withStroke(linewidth=3, foreground="w", alpha=.5)],
)
I am already having Tkinter(someone said to install a tkinter)
code used:
imports are:
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
if u want to view the data-set then it is :
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/diabetes.csv")
code used to plot boxplot in jupyter notebook
fig, ax = plt.subplots(figsize = (20,20))
sns.boxplot(data = df,ax = ax)
)
I was supposed to add in my import's
%matplotlib inline
this is the code(I want to know the peak of the picture but I don't know how to add this kind of code)
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
from scipy import stats
n=25
p=0.6
k=np.arange(0,50)
#the pmf forming
picture=stats.binom.pmf(k,n,p)
print(picture)
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False
mean,var,skew,kurt=stats.binom.stats(n,p,moments='mvsk')
print(mean,var,skew,kurt)
#the picture forming
plt.plot(k,picture,'o-')
plt.grid(True)
plt.show()
You can use scatter
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
from scipy import stats
n=25
p=0.6
k=np.arange(0,50)
#the pmf forming
picture=stats.binom.pmf(k,n,p)
mpl.rcParams['font.sans-serif'] = [u'SimHei']
mpl.rcParams['axes.unicode_minus'] = False
mean,var,skew,kurt=stats.binom.stats(n,p,moments='mvsk')
print(mean,var,skew,kurt)
#the picture forming
plt.plot(k,picture,'o-')
plt.grid(True)
# the two new lines
max_ind = np.argmax(picture)
plt.scatter(x=k[max_ind],y=picture[max_ind],c='r',s=100,zorder=10)
and this produces
my code for df.interpolate was:
import pandas as pd
import numpy as np
import xlrd
from IPython.display import display
from scipy import interpolate
pd.set_option('display.max_rows',54100)
df = pd.read_excel(r'C:\Users\User\Desktop\tanvir random practice\gazipur.xlsx', parse_date=["DateTime"], index_col='DateTime']
df.interpolate(method="linear").bfill()
display(df)
I'd like to change the spacing of the horizontal grid lines on a seaborn chart, I've tried setting the style with no luck:
seaborn.set_style("whitegrid", {
"ytick.major.size": 0.1,
"ytick.minor.size": 0.05,
'grid.linestyle': '--'
})
bar(range(len(data)),data,alpha=0.5)
plot(avg_line)
The gridlines are set automatically desipite me trying to overide the tick size
Any suggestions? Thanks!
you can set the tick locations explicitly later, and it will draw the grid at those locations.
The neatest way to do this is to use a MultpleLocator from the matplotlib.ticker module.
For example:
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
sns.set_style("whitegrid", {'grid.linestyle': '--'})
fig,ax = plt.subplots()
ax.bar(np.arange(0,50,1),np.random.rand(50)*0.016-0.004,alpha=0.5)
ax.yaxis.set_major_locator(ticker.MultipleLocator(0.005))
plt.show()
The OP asked about modifying tick distances in Seaborn.
If you are working in Seaborn and you use a plotting feature that returns an Axes object, then you can work with that just like any other Axes object in matplotlib. For example:
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
from matplotlib.ticker import MultipleLocator
df = sm.datasets.get_rdataset("Guerry", "HistData").data
ax = sns.scatterplot('Literacy', 'Lottery', data=df)
ax.yaxis.set_major_locator(MultipleLocator(10))
ax.xaxis.set_major_locator(MultipleLocator(10))
plt.show()
Put if you are working with one of the Seaborn processes that involve FacetGrid objects, you will see precious little help on how to modify the tick marks without manually setting them. You have dig out the Axes object from the numpy array inside FacetGrid.axes .
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.ticker import MultipleLocator
tips = sns.load_dataset("tips")
g = sns.lmplot(x="total_bill", y="tip", hue="smoker", data=tips, )
g.axes[0][0].yaxis.set_major_locator(MultipleLocator(3))
Note the double subscript required. g is a FacetGrid object, which holds a two-dimensional numpy array of dtype=object, whose entries are matplotlib AxesSubplot objects.
If you are working with a FacetGrid that has multiple axes, then each one will have to be extracted and modified.