I have a table of grades and I want all of the bins to be of the same width
i want the bins to be in the range of [0,56,60,65,70,80,85,90,95,100]
when the first bin is from 0-56 then 56-60 ... with the same width
sns.set_style('darkgrid')
newBins = [0,56,60,65,70,80,85,90,95,100]
sns.displot(data= scores , bins=newBins)
plt.xlabel('grade')
plt.xlim(0,100)
plt.xticks(newBins);
Expected output
how I can balance the width of the bins?
You need to cheat a bit. Define you own bins and name the bins with a linear range. Here is an example:
s = pd.Series(np.random.randint(100, size=100000))
bins = [-0.1, 50, 75, 95, 101]
s2 = pd.cut(s, bins=bins, labels=range(len(bins)-1))
ax = s2.astype(int).plot.hist(bins=len(bins)-
1)
ax.set_xticks(np.linspace(0, len(bins)-2, len(bins)))
ax.set_xticklabels(bins)
Output:
Old answer:
Why don't you let seaborn pick the bins for you:
sns.displot(data=scores, bins='auto')
Or set the number of bins that you want:
sns.displot(data=scores, bins=10)
They will be evenly distributed
You assigning a list to the bins argument of sns.distplot(). This specifies the edges of bins. Since these edges are not spaced evenly, the widths of bins vary.
I think that you may want to use a bar plot (sbs.barplot()) and not a histogram. You would need to compute how many data points are in each bin, and then plot bars without the information what range of values each bar represents. Something like this:
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('darkgrid')
import numpy as np
# sample data
data = np.random.randint(0, 100, 200)
newBins = [0,56,60,65,70,80,85,90,95,100]
# compute bar heights
hist, _ = np.histogram(data, bins=newBins)
# plot a bar diagram
sns.barplot(x = list(range(len(hist))), y = hist)
plt.show()
It gives:
just change the list of values that are you using as binds:
newBins = numpy.arange(0, 100, 1)
You can use bin parameter from histplots but to get exact answer you have to use pd.cut() to creating your own bins.
np.random.seed(101)
df = pd.DataFrame({'scores':pd.Series(np.random.randint(100,size=175)),
'bins_created':pd.cut(scores,bins=[0,55,60,65,70,75,80,85,90,95,100])})
new_data = df['bins_created'].value_counts()
plt.figure(figsize=(10,5),dpi=100)
plots = sns.barplot(x=new_data.index,y=new_data.values)
plt.xlabel('grades')
plt.ylabel('counts')
for bar in plots.patches:
plots.annotate(format(bar.get_height(), '.2f'),
(bar.get_x() + bar.get_width() / 2,
bar.get_height()), ha='center', va='center',
size=10, xytext=(0,5),
textcoords='offset points')
plt.show()
Related
I would like to see both the density and frequency on my histogram. For example, display density on the left side and frequency on the right side.
Here is my code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
x = [6.950915827194559, 0.5704464713012669, -1.655326152372283, 5.867122206816244, -1.809359944941513, -6.164821482653027, -2.538999462076397, 0.2108693568484643, -8.740600769897465, 2.121232876712331, 7.967032967032961, 10.61701196601832, 1.847419201771516, 0.6858006670780847, -2.008695652173909, 2.86991153132885, 1.703131050506168, -1.346913193356314, 3.334927671049193, -15.64688995215311, 20.00022688856367, 10.05956454173731, 2.044936877124148, 3.06513409961684, -0.9973614775725559, 1.190631873030967, -1.509991311902692, -0.3333827233664155, 1.898473282442747, 1.618299899267539, -0.1897860593512823, 1.000000000000001, 3.03501945525293, -7.646697418593529, -0.9769069279216391, -2.918403811792736, -3.90929422276739, 9.609846259653532, 3.240690674452962, 10.08973134408675, 1.98356309650054, 1.915301127899549, -0.7792207792207684, -3.308682400714091, -3.312977099236647, 19.98101265822785, 3.661973444534827, -5.770676691729326, 0.5268044012063156, -1.573767040370533, 3.234974862888484, -1.514352732634994, 6.564849624060143, 9.956794019127146, 3.232590278195024, 2.042007001166857, 1.601164483260553, -2.384737678855331, -2.731242556570068, 0.6069707315088602, 1.40561881957264, -6.805306861851957, 2.492102492102499, -3.639688275501762, 0.7958485384154335, 2.799187725631769, 0.9195966872689088, -2.366608280379856, 0.797679477882518, -3.80380434782609]
df = pd.DataFrame(x, columns=["Returns"])
def plot_histogram():
bins = range(-11, 12, 1)
bins_str = []
for i in bins:
bins_str.append(str(i)+"%")
fig, ax = plt.subplots(figsize=(9, 5))
_, bins, patches = plt.hist(np.clip(df.Returns, bins[0], bins[-1]),
bins=bins, density=True, rwidth=0.8)
xlabels = bins_str[:]
xlabels[-1] = "Over"
xlabels[0] = "Under"
N_labels = len(xlabels)
plt.xlim([bins[0], bins[-1]])
plt.xticks(bins)
ax.set_xticklabels(xlabels)
plt.title("Returns distribution")
plt.grid(axis="y", linewidth=0.5)
plot_histogram()
I tried adding density=True in plt.hist() but it removes the count from the histogram. Is it possible to display both the frequency and density on the same histogram?
A density plot sets the heights of the bars such that the area of all the bars (taking rwidth=1 for that calculation) sums to 1. As such, the bar heights of a counting histogram get divided by (the number of values times the bar widths).
With that conversion factor, you can recalculate the counts from the density (or vice versa). The recalculation can be used to label the bars and/or set a secondary y-axis. Note that the ticks of both y axes are aligned, so the grid only works well for one of them. (A secondary y-axis is a bit different from ax.twiny(), as the former has a fixed conversion between both y axes).
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
x = [6.950915827194559, 0.5704464713012669, -1.655326152372283, 5.867122206816244, -1.809359944941513, -6.164821482653027, -2.538999462076397, 0.2108693568484643, -8.740600769897465, 2.121232876712331, 7.967032967032961, 10.61701196601832, 1.847419201771516, 0.6858006670780847, -2.008695652173909, 2.86991153132885, 1.703131050506168, -1.346913193356314, 3.334927671049193, -15.64688995215311, 20.00022688856367, 10.05956454173731, 2.044936877124148, 3.06513409961684, -0.9973614775725559, 1.190631873030967, -1.509991311902692, -0.3333827233664155, 1.898473282442747, 1.618299899267539, -0.1897860593512823, 1.000000000000001, 3.03501945525293, -7.646697418593529, -0.9769069279216391, -2.918403811792736, -3.90929422276739, 9.609846259653532, 3.240690674452962, 10.08973134408675, 1.98356309650054, 1.915301127899549, -0.7792207792207684, -3.308682400714091, -3.312977099236647, 19.98101265822785, 3.661973444534827, -5.770676691729326, 0.5268044012063156, -1.573767040370533, 3.234974862888484, -1.514352732634994, 6.564849624060143, 9.956794019127146, 3.232590278195024, 2.042007001166857, 1.601164483260553, -2.384737678855331, -2.731242556570068, 0.6069707315088602, 1.40561881957264, -6.805306861851957, 2.492102492102499, -3.639688275501762, 0.7958485384154335, 2.799187725631769, 0.9195966872689088, -2.366608280379856, 0.797679477882518, -3.80380434782609]
df = pd.DataFrame(x, columns=["Returns"])
bins = range(-11, 12, 1)
bins_str = [str(i) + "%" for i in bins]
fig, ax = plt.subplots(figsize=(9, 5))
values, bins, patches = ax.hist(np.clip(df["Returns"], bins[0], bins[-1]),
bins=bins, density=True, rwidth=0.8)
# conversion between counts and density: number of values times bin width
factor = len(df) * (bins[1] - bins[0])
ax.bar_label(patches, ['' if v == 0 else f'{v * factor:.0f}' for v in values])
xlabels = bins_str[:]
xlabels[-1] = "Over"
xlabels[0] = "Under"
ax.set_xlim([bins[0], bins[-1]])
ax.set_xticks(bins, xlabels)
ax.set_title("Returns distribution")
ax.grid(axis="y", linewidth=0.5)
secax = ax.secondary_yaxis('right', functions=(lambda y: y * factor, lambda y: y / factor))
secax.set_ylabel('counts')
ax.set_ylabel('density')
plt.show()
To have the same grid positions for both y-axes, you can copy the ticks of one and convert them to set them at the other. For the ticks to be calculated, the plot needs to be drawn once (at the end of the code). Note that the converted values are only shown with a limited number of digits.
fig.canvas.draw()
ax.set_yticks(secax.get_yticks() / factor)
plt.show()
Hi I'm trying to plot a pointplot and scatterplot on one graph with the same dataset so I can see the individual points that make up the pointplot.
Here is the code I am using:
xlPath = r'path to data here'
df = pd.concat(pd.read_excel(xlPath, sheet_name=None),ignore_index=True)
sns.pointplot(data=df, x='ID', y='HM (N/mm2)', palette='bright', capsize=0.15, alpha=0.5, ci=95, join=True, hue='Layer')
sns.scatterplot(data=df, x='ID', y='HM (N/mm2)')
plt.show()
When I plot, for some reason the points from the scatterplot are offsetting one ID spot right on the x-axis. When I plot the scatter or the point plot separately, they each are in the correct ID spot. Why would plotting them on the same plot cause the scatterplot to offset one right?
Edit: Tried to make the ID column categorical, but that didn't work either.
Seaborn's pointplot creates a categorical x-axis while here the scatterplot uses a numerical x-axis.
Explicitly making the x-values categorical: df['ID'] = pd.Categorical(df['ID']), isn't sufficient, as the scatterplot still sees numbers. Changing the values to strings does the trick. To get them in the correct order, sorting might be necessary.
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
# first create some test data
df = pd.DataFrame({'ID': np.random.choice(np.arange(1, 49), 500),
'HM (N/mm2)': np.random.uniform(1, 10, 500)})
df['Layer'] = ((df['ID'] - 1) // 6) % 4 + 1
df['HM (N/mm2)'] += df['Layer'] * 8
df['Layer'] = df['Layer'].map(lambda s: f'Layer {s}')
# sort the values and convert the 'ID's to strings
df = df.sort_values('ID')
df['ID'] = df['ID'].astype(str)
fig, ax = plt.subplots(figsize=(12, 4))
sns.pointplot(data=df, x='ID', y='HM (N/mm2)', palette='bright',
capsize=0.15, alpha=0.5, ci=95, join=True, hue='Layer', ax=ax)
sns.scatterplot(data=df, x='ID', y='HM (N/mm2)', color='purple', ax=ax)
ax.margins(x=0.02)
plt.tight_layout()
plt.show()
I am plotting a histogram using matplotlib but my y-axis range is in the millions. How can I scale the y-axis so that instead of printing 5000000 it will print 5
Here is my code
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
filename = './norstar10readlength.csv'
df=pd.read_csv(filename, sep=',',header=None)
n, bins, patches = plt.hist(x=df.values, bins=10, color='#0504aa',
alpha=0.7, rwidth=0.85)
plt.grid(axis='y', alpha=0.75)
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.title('My Very Own Histogram')
maxfreq = n.max()
# Set a clean upper y-axis limit.
plt.ylim(ymax=np.ceil(maxfreq / 10) * 10 if maxfreq % 10 else maxfreq + 10)
plt.show()
And here is the plot I am generating now
An elegant solution is to apply a FuncFormatter to format y labels.
Instead of your source data, I used the following DataFrame:
Val
0 800000
1 2600000
2 6700000
3 1400000
4 1700000
5 1600000
and made a bar plot. "Ordinary" bar plot:
df.Val.plot.bar(rot=0, width=0.75);
yields a picture with original values on the y axis (1000000 to
7000000).
But if you run:
from matplotlib.ticker import FuncFormatter
def lblFormat(n, pos):
return str(int(n / 1e6))
lblFormatter = FuncFormatter(lblFormat)
ax = df.Val.plot.bar(rot=0, width=0.75)
ax.yaxis.set_major_formatter(lblFormatter)
then y axis labels are integers (the number of millions):
So you can arrange your code something like this:
n, bins, patches = plt.hist(x=df.values, ...)
#
# Other drawing actions, up to "plt.ylim" (including)
#
ax = plt.gca()
ax.yaxis.set_major_formatter(lblFormatter)
plt.show()
You can modify your df itself, you just need to decide one ratio
so if you want to make 50000 to 5 then it means the ratio is 5/50000 which is 0.0001
Once you have the ratio just multiply all the values of y-axis with the ratio in your DataFrame itself.
Hope this helps!!
I want to plot scatter points corresponding to 6 different datasets over global maps of the Earth. The problem is that some of these quantities have negative values and they don't appear in the maps. I have tried to overcome this problem by taking absolute values of the data and multiplying (or taking the power of) them by some factors, but nothing seems to work the way I want. The problem is that the datasets have very different ranges. Ideally, I want them all to have the same scale so everything will be more organized, but I don't know how to do this.
I created some synthetic data to illustrate this issue
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.basemap import Basemap, addcyclic, shiftgrid
from matplotlib.pyplot import cm
np.random.seed(100)
VarReTx = np.random.uniform(low=-0.087, high=0.0798, size=(52,))
VarReTy = np.random.uniform(low=-0.076, high=0.1919, size=(52,))
VarImTx = np.random.uniform(low=-0.0331, high=0.0527, size=(52,))
VarImTy = np.random.uniform(low=-0.0311, high=0.2007, size=(52,))
eTx = np.random.uniform(low=0.0019, high=0.0612, size=(52,))
eTx = np.random.uniform(low=0.0031, high=0.0258, size=(52,))
obslat = np.array([18.62, -65.25, -13.8, -7.95, -23.77, 51.84, 40.14, 58.07,
-12.1875, -35.32, 36.37, -46.43, 40.957, -43.474, 38.2 , 37.09,
48.17, 0.6946, 13.59, 28.32, 51., -25.88, -34.43, 21.32,
-12.05, 52.27, 36.23, -12.69, 31.42, 5.21, -22.22, 36.1,
14.38, -54.5, 43.91, 61.16, 48.27, 52.07, 54.85, 45.403,
52.971, -17.57, -51.7, 18.11, 39.55, 47.595, 22.79, -37.067,
-1.2, 32.18, 51.933, 48.52])
obslong = np.array([-287.13, -64.25, -171.78, -14.38, -226.12, -339.21, -105.24,
-321.77, -263.1664, -210.64, -233.146, -308.13, -359.667, -187.607,
-77.37, -119.72, -348.72, -287.8463, -215.13, -16.43, -4.48,
-332.29, -340.77, -158., -75.33, -255.55, -219.82, -227.53,
-229.12, -52.73, -245.9, -256.16, -16.97, -201.05, -215.81,
-45.442, -117.12, -347.32, -276.77, -75.552, -201.752, -149.58,
-57.89, -66.15, -4.35, -52.677, -354.47, -12.315, -48.5,
-110.73, -10.25, -123.42, ])
fig, ([ax1, ax2], [ax3, ax4], [eax1, eax2]) = plt.subplots(3,2, figsize=(24,23))
matplotlib.rc('xtick', labelsize=12)
matplotlib.rc('ytick', labelsize=12)
plots = [ax1, ax2, ax3, ax4, eax1, eax2]
Vars = [VarReTx, VarReTy, VarImTx, VarImTy, eTx, eTy]
titles = [r'$\Delta$ ReTx', r'$\Delta$ ReTy', r'$\Delta$ ImTx', r'$\Delta$ ImTy', 'Error (X)', 'Error (Y)']
colors = iter(cm.jet(np.reshape(np.linspace(0.0, 1.0, len(plots)), ((len(plots), 1)))))
for j in range(len(plots)):
c3 = next(colors)
lat = np.arange(-91, 91, 0.5)
long = np.arange(-0.1, 360.1, 0.5)
longrid, latgrid = np.meshgrid(long, lat)
plots[j].set_title(titles[j], fontsize=48, y=1.05)
condmap = Basemap(projection='robin', llcrnrlat=-90, urcrnrlat=90,\
llcrnrlon=-180, urcrnrlon=180, resolution='c', lon_0=0, ax=plots[j])
maplong, maplat = condmap(longrid, latgrid)
condmap.drawcoastlines()
condmap.drawmapboundary(fill_color='white')
parallels = np.arange(-90, 90, 15)
condmap.drawparallels(parallels,labels=[False,True,True,False], fontsize=15)
x,y = condmap(obslong, obslat)
w = []
for m in range(obslong.size):
w.append(Vars[j][m])
w = np.array(w)
condmap.scatter(x, y, s = w*1e+4, c=c3)
r = np.linspace(np.min(Vars[j]), np.max(Vars[j]), 4)
for n in r:
condmap.scatter([], [], c=c3, s=n*1e+4, label=str(np.round(n, 4)))
plots[j].legend(bbox_to_anchor=(0., -0.2, 1., .102), loc='lower left',
ncol=4, mode="expand", borderaxespad=0., fontsize=16, frameon = False)
plt.show()
plt.close('all')
As you can see in the map, negative data does not are not being exhibited. I want they all to appear in the maps and that all the scatter plots have the same scale in their respective ranges. Thanks!
It looks like you are trying to map your dataset to dot size. Obviously you cannot have negative size dots, so that won't work.
Instead, you need to normalize your dataset to a strictly positive range and use those normalized values for the size parameter. A simple way to do this would be to use matplotlib.colors.Normalize(vmin, vmax), which allows you to map any values in the interval [vmin, vmax] to the interval [0,1].
If you want to have a shared scale for all your datasets, first find the global min and max, and use that to instantiate your normalization, then normalize each dataset when plotting:
datasets = [VarReTx,VarReTy,VarImTx,VarImTy,eTx,eTx]
min_val = min([d.min() for d in datasets])
max_val = max([d.max() for d in datasets])
norm = matplotlib.colors.Normalize(vmin=min_val, vmax=max_val)
plt.scatter(x,y,s=norm(VarReTx)*100) # choose appropiate scaling factor instead of 100 to get nicely sized dots
I am trying to generate a histogram using matplotlib. I am reading data from the following file:
https://github.com/meghnasubramani/Files/blob/master/class_id.txt
My intent is to generate a histogram with the following bins: 1, 2-5, 5-100, 100-200, 200-1000, >1000.
When I generate the graph it doesn't look nice.
I would like to normalize the y axis to (frequency of occurrence in a bin/total items). I tried using the density parameter but whenever I try that my graph ends up completely blank. How do I go about doing this.
How do I get the width's of the bars to be the same, even though the bin ranges are varied?
Is it also possible to specify the ticks on the histogram? I want to have the ticks correspond to the bin ranges.
import matplotlib.pyplot as plt
FILE_NAME = 'class_id.txt'
class_id = [int(line.rstrip('\n')) for line in open(FILE_NAME)]
num_bins = [1, 2, 5, 100, 200, 1000, max(class_id)]
x = plt.hist(class_id, bins=num_bins, histtype='bar', align='mid', rwidth=0.5, color='b')
print (x)
plt.legend()
plt.xlabel('Items')
plt.ylabel('Frequency')
As suggested by importanceofbeingernest, we can use bar charts to plot categorical data and we need to categorize values in bins, for ex with pandas:
import matplotlib.pyplot as plt
import pandas
FILE_NAME = 'class_id.txt'
class_id_file = [int(line.rstrip('\n')) for line in open(FILE_NAME)]
num_bins = [0, 2, 5, 100, 200, 1000, max(class_id_file)]
categories = pandas.cut(class_id_file, num_bins)
df = pandas.DataFrame(class_id_file)
dfg = df.groupby(categories).count()
bins_labels = ["1-2", "2-5", "5-100", "100-200", "200-1000", ">1000"]
plt.bar(range(len(categories.categories)), dfg[0]/len(class_id_file), tick_label=bins_labels)
#plt.bar(range(len(categories.categories)), dfg[0]/len(class_id_file), tick_label=categories.categories)
plt.xlabel('Items')
plt.ylabel('Frequency')
Not what you asked for, but you could also stay with histogram and choose logarithm scale to improve readability:
plt.xscale('log')