In this data set I need to plot,pH as the x-column which is having continuous data and need to group it together the pH axis as per the quality value and plot the histogram. In many of the resources I referred I found solutions for using random data generated. I tried this piece of code.
plt.hist(, density=True, bins=1)
plt.ylabel('quality')
plt.xlabel('pH');
Where I eliminated the random generated data, but I received and error
File "<ipython-input-16-9afc718b5558>", line 1
plt.hist(, density=True, bins=1)
^
SyntaxError: invalid syntax
What is the proper way to plot my data?I want to feed into the histogram not randomly generated data, but data found in the data set.
Your Error
The immediate problem in your code is the missing data to the plt.hist() command.
plt.hist(, density=True, bins=1)
should be something like:
plt.hist(data_table['pH'], density=True, bins=1)
Seaborn histplot
But this doesn't get the plot broken down by quality. The answer by Mr.T looks correct, but I'd also suggest seaborn which works with "melted" data like you have. The histplot command should give you what you want:
import seaborn as sns
sns.histplot(data=df, x="pH", hue="quality", palette="Dark2", element='step')
Assuming the table you posted is in a pandas.DataFrame named df with columns "pH" and "quality", you get something like:
The palette (Dark2) can can be any matplotlib colormap.
Subplots
If the overlaid histograms are too hard to see, an option is to do facets or small multiples. To do this with pandas and matplotlib:
# group dataframe by quality values
data_by_qual = df.groupby('quality')
# create a sub plot for each quality group
fig, axes = plt.subplots(nrows=len(data_by_qual),
figsize=[6,12],
sharex=True)
fig.subplots_adjust(hspace=.5)
# loop over axes and quality groups together
for ax, (quality, qual_data) in zip(axes, data_by_qual):
ax.hist(qual_data['pH'], bins=10)
ax.set_title(f"quality = {quality}")
ax.set_xlabel('pH')
Altair Facets
The plotting library altair can do this for you:
import altair as alt
alt.Chart(df).mark_bar().encode(
alt.X("pH:Q", bin=True),
y='count()',
).facet(row='quality')
Several possibilities here to represent multiple histograms. All have in common that the data have to be transformed from long to wide format - meaning, each category is in its own column:
import matplotlib.pyplot as plt
import pandas as pd
#test data generation
import numpy as np
np.random.seed(123)
n=300
df = pd.DataFrame({"A": np.random.randint(1, 100, n), "pH": 3*np.random.rand(n), "quality": np.random.choice([3, 4, 5, 6], n)})
df.pH += df.quality
#instead of this block you have to read here your stored data, e.g.,
#df = pd.read_csv("my_data_file.csv")
#check that it read the correct data
#print(df.dtypes)
#print(df.head(10))
#bringing the columns in the required wide format
plot_df = df.pivot(columns="quality")["pH"]
bin_nr=5
#creating three subplots for different ways to present the same histograms
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(6, 12))
ax1.hist(plot_df, bins=bin_nr, density=True, histtype="bar", label=plot_df.columns)
ax1.legend()
ax1.set_title("Basically bar graphs")
plot_df.plot.hist(stacked=True, bins=bin_nr, density=True, ax=ax2)
ax2.set_title("Stacked histograms")
plot_df.plot.hist(alpha=0.5, bins=bin_nr, density=True, ax=ax3)
ax3.set_title("Overlay histograms")
plt.show()
Sample output:
It is not clear, though, what you intended to do with just one bin and why your y-axis was labeled "quality" when this axis represents the frequency in a histogram.
I have a data that looks like a sigmoidal plot but flipped relative to the vertical line.
But the plot is a result of plotting 1D data instead of some sort of function.
My goal is to find the x value when the y value is at 50%. As you can see, there is no data point when y is exactly at 50%.
Interpolate comes to my mind. But I'm not sure if interpolate enable me to find the x value when the y value is 50%. So my question is 1) can you use interpolate to find the x when the y is 50%? or 2)do you need to fit the data to some sort of a function?
Below is what I currently have in my code
import numpy as np
import matplotlib.pyplot as plt
my_x = [4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66]
my_y_raw=np.array([0.99470977497817203, 0.99434995886145172, 0.98974611323163653, 0.961630837657524, 0.99327633558441175, 0.99338952769251909, 0.99428263292577534, 0.98690514212711611, 0.99111667721533181, 0.99149418924880861, 0.99133773062680464, 0.99143506380003499, 0.99151080464011454, 0.99268261743308517, 0.99289757252812316, 0.99100207861144063, 0.99157171773324027, 0.99112571824824358, 0.99031608691035722, 0.98978104266076905, 0.989782674787969, 0.98897835092187614, 0.98517540405423909, 0.98308943666187076, 0.96081810781994603, 0.85563541881892147, 0.61570811548079107, 0.33076276040577052, 0.14655134838124245, 0.076853147122142126, 0.035831324928136087, 0.021344669212790181])
my_y=my_y_raw/np.max(my_y_raw)
plt.plot(my_x, my_y,color='k', markersize=40)
plt.scatter(my_x,my_y,marker='*',label="myplot", color='k', edgecolor='k', linewidth=1,facecolors='none',s=50)
plt.legend(loc="lower left")
plt.xlim([4,102])
plt.show()
Using SciPy
The most straightforward way to do the interpolation is to use the SciPy interpolate.interp1d function. SciPy is closely related to NumPy and you may already have it installed. The advantage to interp1d is that it can sort the data for you. This comes at the cost of somewhat funky syntax. In many interpolation functions it is assumed that you are trying to interpolate a y value from an x value. These functions generally need the "x" values to be monotonically increasing. In your case, we swap the normal sense of x and y. The y values have an outlier as #Abhishek Mishra has pointed out. In the case of your data, you are lucky and you can get away with the the leaving the outlier in.
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
my_x = [4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,
48,50,52,54,56,58,60,62,64,66]
my_y_raw=np.array([0.99470977497817203, 0.99434995886145172,
0.98974611323163653, 0.961630837657524, 0.99327633558441175,
0.99338952769251909, 0.99428263292577534, 0.98690514212711611,
0.99111667721533181, 0.99149418924880861, 0.99133773062680464,
0.99143506380003499, 0.99151080464011454, 0.99268261743308517,
0.99289757252812316, 0.99100207861144063, 0.99157171773324027,
0.99112571824824358, 0.99031608691035722, 0.98978104266076905,
0.989782674787969, 0.98897835092187614, 0.98517540405423909,
0.98308943666187076, 0.96081810781994603, 0.85563541881892147,
0.61570811548079107, 0.33076276040577052, 0.14655134838124245,
0.076853147122142126, 0.035831324928136087, 0.021344669212790181])
# set assume_sorted to have scipy automatically sort for you
f = interp1d(my_y_raw, my_x, assume_sorted = False)
xnew = f(0.5)
print('interpolated value is ', xnew)
plt.plot(my_x, my_y_raw,'x-', markersize=10)
plt.plot(xnew, 0.5, 'x', color = 'r', markersize=20)
plt.plot((0, xnew), (0.5,0.5), ':')
plt.grid(True)
plt.show()
which gives
interpolated value is 56.81214249272691
Using NumPy
Numpy also has an interp function, but it doesn't do the sort for you. And if you don't sort, you'll be sorry:
Does not check that the x-coordinate sequence xp is increasing. If xp
is not increasing, the results are nonsense.
The only way I could get np.interp to work was to shove the data in to a structured array.
import numpy as np
import matplotlib.pyplot as plt
my_x = np.array([4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,
48,50,52,54,56,58,60,62,64,66], dtype = np.float)
my_y_raw=np.array([0.99470977497817203, 0.99434995886145172,
0.98974611323163653, 0.961630837657524, 0.99327633558441175,
0.99338952769251909, 0.99428263292577534, 0.98690514212711611,
0.99111667721533181, 0.99149418924880861, 0.99133773062680464,
0.99143506380003499, 0.99151080464011454, 0.99268261743308517,
0.99289757252812316, 0.99100207861144063, 0.99157171773324027,
0.99112571824824358, 0.99031608691035722, 0.98978104266076905,
0.989782674787969, 0.98897835092187614, 0.98517540405423909,
0.98308943666187076, 0.96081810781994603, 0.85563541881892147,
0.61570811548079107, 0.33076276040577052, 0.14655134838124245,
0.076853147122142126, 0.035831324928136087, 0.021344669212790181],
dtype = np.float)
dt = np.dtype([('x', np.float), ('y', np.float)])
data = np.zeros( (len(my_x)), dtype = dt)
data['x'] = my_x
data['y'] = my_y_raw
data.sort(order = 'y') # sort data in place by y values
print('numpy interp gives ', np.interp(0.5, data['y'], data['x']))
which gives
numpy interp gives 56.81214249272691
As you said, your data looks like a flipped sigmoidal. Can we make the assumption that your function is a strictly decreasing function? If that is the case, we can try the following methods:
Remove all the points where the data is not strictly decreasing.For example, for your data that point will be near 0.
Use the binary search to find the location where y=0.5 should be put in.
Now you know two (x, y) pairs where your desired y=0.5 should lie.
You can use simple linear interpolation if (x, y) pairs are very close.
Otherwise, you can see what is the approximation of sigmoid near those pairs.
You might not need to fit any functions to your data. Simply find the following two elements:
The largest x for which y<50%
The smallest x for which y>50%
Then use interpolation and find the x*. Below is the code
my_x = np.array([4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66])
my_y=np.array([0.99470977497817203, 0.99434995886145172, 0.98974611323163653, 0.961630837657524, 0.99327633558441175, 0.99338952769251909, 0.99428263292577534, 0.98690514212711611, 0.99111667721533181, 0.99149418924880861, 0.99133773062680464, 0.99143506380003499, 0.99151080464011454, 0.99268261743308517, 0.99289757252812316, 0.99100207861144063, 0.99157171773324027, 0.99112571824824358, 0.99031608691035722, 0.98978104266076905, 0.989782674787969, 0.98897835092187614, 0.98517540405423909, 0.98308943666187076, 0.96081810781994603, 0.85563541881892147, 0.61570811548079107, 0.33076276040577052, 0.14655134838124245, 0.076853147122142126, 0.035831324928136087, 0.021344669212790181])
tempInd1 = my_y<.5 # This will only work if the values are monotonic
x1 = my_x[tempInd1][0]
y1 = my_y[tempInd1][0]
x2 = my_x[~tempInd1][-1]
y2 = my_y[~tempInd1][-1]
scipy.interp(0.5, [y1, y2], [x1, x2])
I can create a simple columnar diagram in a matplotlib according to the 'simple' dictionary:
import matplotlib.pyplot as plt
D = {u'Label1':26, u'Label2': 17, u'Label3':30}
plt.bar(range(len(D)), D.values(), align='center')
plt.xticks(range(len(D)), D.keys())
plt.show()
But, how do I create curved line on the text and numeric data of this dictionarie, I do not know?
Т_OLD = {'10': 'need1', '11': 'need2', '12': 'need1', '13': 'need2', '14': 'need1'}
Like the picture below
You may use numpy to convert the dictionary to an array with two columns, which can be plotted.
import matplotlib.pyplot as plt
import numpy as np
T_OLD = {'10' : 'need1', '11':'need2', '12':'need1', '13':'need2','14':'need1'}
x = list(zip(*T_OLD.items()))
# sort array, since dictionary is unsorted
x = np.array(x)[:,np.argsort(x[0])].T
# let second column be "True" if "need2", else be "False
x[:,1] = (x[:,1] == "need2").astype(int)
# plot the two columns of the array
plt.plot(x[:,0], x[:,1])
#set the labels accordinly
plt.gca().set_yticks([0,1])
plt.gca().set_yticklabels(['need1', 'need2'])
plt.show()
The following would be a version, which is independent on the actual content of the dictionary; only assumption is that the keys can be converted to floats.
import matplotlib.pyplot as plt
import numpy as np
T_OLD = {'10': 'run', '11': 'tea', '12': 'mathematics', '13': 'run', '14' :'chemistry'}
x = np.array(list(zip(*T_OLD.items())))
u, ind = np.unique(x[1,:], return_inverse=True)
x[1,:] = ind
x = x.astype(float)[:,np.argsort(x[0])].T
# plot the two columns of the array
plt.plot(x[:,0], x[:,1])
#set the labels accordinly
plt.gca().set_yticks(range(len(u)))
plt.gca().set_yticklabels(u)
plt.show()
Use numeric values for your y-axis ticks, and then map them to desired strings with plt.yticks():
import matplotlib.pyplot as plt
import pandas as pd
# example data
times = pd.date_range(start='2017-10-17 00:00', end='2017-10-17 5:00', freq='H')
data = np.random.choice([0,1], size=len(times))
data_labels = ['need1','need2']
fig, ax = plt.subplots()
ax.plot(times, data, marker='o', linestyle="None")
plt.yticks(data, data_labels)
plt.xlabel("time")
Note: It's generally not a good idea to use a line graph to represent categorical changes in time (e.g. from need1 to need2). Doing that gives the visual impression of a continuum between time points, which may not be accurate. Here, I changed the plotting style to points instead of lines. If for some reason you need the lines, just remove linestyle="None" from the call to plt.plot().
UPDATE
(per comments)
To make this work with a y-axis category set of arbitrary length, use ax.set_yticks() and ax.set_yticklabels() to map to y-axis values.
For example, given a set of potential y-axis values labels, let N be the size of a subset of labels (here we'll set it to 4, but it could be any size).
Then draw a random sample data of y values and plot against time, labeling the y-axis ticks based on the full set labels. Note that we still use set_yticks() first with numerical markers, and then replace with our category labels with set_yticklabels().
labels = np.array(['A','B','C','D','E','F','G'])
N = 4
# example data
times = pd.date_range(start='2017-10-17 00:00', end='2017-10-17 5:00', freq='H')
data = np.random.choice(np.arange(len(labels)), size=len(times))
fig, ax = plt.subplots(figsize=(15,10))
ax.plot(times, data, marker='o', linestyle="None")
ax.set_yticks(np.arange(len(labels)))
ax.set_yticklabels(labels)
plt.xlabel("time")
This gives the exact desired plot:
import matplotlib.pyplot as plt
from collections import OrderedDict
T_OLD = {'10' : 'need1', '11':'need2', '12':'need1', '13':'need2','14':'need1'}
T_SRT = OrderedDict(sorted(T_OLD.items(), key=lambda t: t[0]))
plt.plot(map(int, T_SRT.keys()), map(lambda x: int(x[-1]), T_SRT.values()),'r')
plt.ylim([0.9,2.1])
ax = plt.gca()
ax.set_yticks([1,2])
ax.set_yticklabels(['need1', 'need2'])
plt.title('T_OLD')
plt.xlabel('time')
plt.ylabel('need')
plt.show()
For Python 3.X the plotting lines needs to explicitly convert the map() output to lists:
plt.plot(list(map(int, T_SRT.keys())), list(map(lambda x: int(x[-1]), T_SRT.values())),'r')
as in Python 3.X map() returns an iterator as opposed to a list in Python 2.7.
The plot uses the dictionary keys converted to ints and last elements of need1 or need2, also converted to ints. This relies on the particular structure of your data, if the values where need1 and need3 it would need a couple more operations.
After plotting and changing the axes limits, the program simply modifies the tick labels at y positions 1 and 2. It then also adds the title and the x and y axis labels.
Important part is that the dictionary/input data has to be sorted. One way to do it is to use OrderedDict. Here T_SRT is an OrderedDict object sorted by keys in T_OLD.
The output is:
This is a more general case for more values/labels in T_OLD. It assumes that the label is always 'needX' where X is any number. This can readily be done for a general case of any string preceding the number though it would require more processing,
import matplotlib.pyplot as plt
from collections import OrderedDict
import re
T_OLD = {'10' : 'need1', '11':'need8', '12':'need11', '13':'need1','14':'need3'}
T_SRT = OrderedDict(sorted(T_OLD.items(), key=lambda t: t[0]))
x_val = list(map(int, T_SRT.keys()))
y_val = list(map(lambda x: int(re.findall(r'\d+', x)[-1]), T_SRT.values()))
plt.plot(x_val, y_val,'r')
plt.ylim([0.9*min(y_val),1.1*max(y_val)])
ax = plt.gca()
y_axis = list(set(y_val))
ax.set_yticks(y_axis)
ax.set_yticklabels(['need' + str(i) for i in y_axis])
plt.title('T_OLD')
plt.xlabel('time')
plt.ylabel('need')
plt.show()
This solution finds the number at the end of the label using re.findall to accommodate for the possibility of multi-digit numbers. Previous solution just took the last component of the string because numbers were single digit. It still assumes that the number for plotting position is the last number in the string, hence the [-1]. Again for Python 3.X map output is explicitly converted to list, step not necessary in Python 2.7.
The labels are now generated by first selecting unique y-values using set and then renaming their labels through concatenation of the strings 'need' with its corresponding integer.
The limits of y-axis are set as 0.9 of the minimum value and 1.1 of the maximum value. Rest of the formatting is as before.
The result for this test case is: