How to fix "Key Error :'not in index' " error in Jupyter Notebooks - python-3.x

The code is to plot the colored graph of the total number of cases in red and recovered cases in green.
There is a key error of not in the index, I checked the spelling and also the syntax, Stil can't find the solution.
#Visulizating Data using Seaborn
f, ax = plt.subplots(figsize=(12, 8))
data = df_full[['Name of the State / UT', 'Total Confirmed Cases (Indian National)', 'Recovered',
'Deaths']]
data.sort_values('Total cases', ascending=False, inplace=True)
sns.set_color_codes("pastel")
sns.barplot(x="Total Confirmed Cases (Indian National)", y="Name of the State / UT",
data=data,lable="Total", color="r")
sns.set_color_codes("muted")
sns.barplot(x="Recovred", y="Name of the State / UT", data=data,label="Reccovered", color="g")
#ADD a legend and informative axis label
ax.legend(ncol=2, loc="lower right", frameon=True)
ax.set(xlim=(0, 35), ylabel="", xlabel="Cases")
sns.despine(left=True, bottom=True)
**ERROR**
KeyError: "['Name of the State / UT'] not in index"

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cg16189596,0,0,0,2
And I want to create a seaborn heatmap like this:
plt.figure(figsize=(470, 60))
sns.set(font_scale = 14)
df=comparison.T
# create a Boolean mask of df
mask = df.ge(1).all()
# use the mask to update a list of labels
cols = [col if m else '' for (col, m) in zip(df.columns, mask)]
# plot with custom labels
ax = sns.heatmap(df, xticklabels=cols,cmap="crest_r")
ax.set_xticklabels(labels=cols, fontsize=200)
plt.show()
However, sometimes due to the narrow space the xtick labels overlap. Is there any way to add more spacing while still providing a readable image (not too small so that it cannot be read) or to put them one below the other?

Remove repeating values from X axis label in Altair

I am having trouble with Altair repeating X axis label values.
Data:
rule_abbreaviation flagged_claim bill_month
0 CONCIDPROC 1 Apr2022
1 CONTUSMAT1 1 Apr2022
2 COVID05 1 Jun2021
3 FILTROTUB2 1 Sep2021
4 MEPIARTRO1 1 Mar2022
#Code to generate Altair Bar Chart
bar = alt.Chart(Data).mark_bar().encode(
x=alt.X('flagged_claim:Q', axis=alt.Axis(title='Flagged Claims', format= ',.0f'), stack='zero'),
y=alt.Y('rule_abbreaviation:N', axis=alt.Axis(title='Component Abbreviation'), sort=alt.SortField(field=measure, order='descending')),
tooltip=[alt.Tooltip('max(ClaimRuleName):N', title='Claim Component'), alt.Tooltip('flagged_claim:Q', title='Flagged Claims', format= ',.0f')],
color=alt.Color('bill_month', legend=None)
).properties(width=485,
title = alt.TitleParams(text = 'Bottom Components',
font = 'Arial',
fontSize = 16,
color = '#000080',
)
).interactive()
X axis label generated by this chart contains repeated 0 and 1
Image of Visualization: https://i.stack.imgur.com/0XdWB.png

The reason this is happening is because you have format= ',.0f' which tells Altair to include 0 decimals in the axis labels. Remove it or change to 1f to see decimals in the labels. In general, a good way to troubleshoot problems like this is to remove part of your code at a time to identify which part is causing the unexpected behavior.
To reduce the number of ticks you can use alt.Axis(title='Flagged Claims', format='d', tickCount=1) or alt.Axis(title='Flagged Claims', format='d', values=[0, 1]). See also Changing Number of y-axis Ticks in Altair

How does Elevation of a Head Pose in Python-OpenCV work?

I am trying to estimate the head pose of single images mostly following this guide:
https://towardsdatascience.com/real-time-head-pose-estimation-in-python-e52db1bc606a
The detection of the face works fine - if i plot the image and the detected landmarks they line up nicely.
I am estimating the camera matrix from the image, and assume no lens distortion:
size = image.shape
focal_length = size[1]
center = (size[1]/2, size[0]/2)
camera_matrix = np.array([[focal_length, 0, center[0]],
[0, focal_length, center[1]],
[0, 0, 1]], dtype="double")
dist_coeffs = np.zeros((4, 1)) # Assuming no lens distortion
I am trying to get the head pose by matching points in the image with points in the 3D model using solvePNP:
# 3D-model points to which the points extracted from an image are matched:
model_points = np.array([
(0.0, 0.0, 0.0), # Nose tip
(0.0, -330.0, -65.0), # Chin
(-225.0, 170.0, -135.0), # Left eye corner
(225.0, 170.0, -135.0), # Right eye corner
(-150.0, -150.0, -125.0), # Left Mouth corner
(150.0, -150.0, -125.0) # Right mouth corner
])
image_points = np.array([
shape[30], # Nose tip
shape[8], # Chin
shape[36], # Left eye left corner
shape[45], # Right eye right corne
shape[48], # Left Mouth corner
shape[54] # Right mouth corner
], dtype="double")
success, rotation_vec, translation_vec) = \
cv2.solvePnP(model_points, image_points, camera_matrix, dist_coeffs)
finally, I am getting the euler angles from the rotation:
rotation_mat, _ = cv2.Rodrigues(rotation_vec)
pose_mat = cv2.hconcat((rotation_mat, translation_vec))
_, _, _, _, _, _, angles = cv2.decomposeProjectionMatrix(pose_mat)
now the azimuth is what i would expect - it is negative if i look to the left, zero in the middle and positive to the right.
the elevation however is strange - if i look in the middle it has a constant value but the sign is random - changing from image to image (the value is around 170).
When i look up the sign is positive and the value decreases the more i look up,
When i look down the sign is negative and the value decreases the more i look down.
Can someone explain this output to me?

Ok so it seems i have found a solution - the model points (which i have found in several blogs on the topic) seem to be wrong. The code seems to work with this combination of model and image points (no idea why it was trial and error):
model_points = np.float32([[6.825897, 6.760612, 4.402142],
[1.330353, 7.122144, 6.903745],
[-1.330353, 7.122144, 6.903745],
[-6.825897, 6.760612, 4.402142],
[5.311432, 5.485328, 3.987654],
[1.789930, 5.393625, 4.413414],
[-1.789930, 5.393625, 4.413414],
[-5.311432, 5.485328, 3.987654],
[2.005628, 1.409845, 6.165652],
[-2.005628, 1.409845, 6.165652],
[2.774015, -2.080775, 5.048531],
[-2.774015, -2.080775, 5.048531],
[0.000000, -3.116408, 6.097667],
[0.000000, -7.415691, 4.070434]])
image_points = np.float32([shape[17], shape[21], shape[22], shape[26],
shape[36], shape[39], shape[42], shape[45],
shape[31], shape[35], shape[48], shape[54],
shape[57], shape[8]])

What is the proper way to employ date2num for timestamps using candlestick_ohlc

My data looks like this (Date, Open, High, Low, Close):
ohlc = [
[1502929058, 1.2652, 1.2653, 1.265, 1.2653],
[1502929059, 1.267, 1.267, 1.267, 1.267],
[1502929060, 1.2655, 1.2656, 1.2655, 1.2656],
[1502929061, 1.2652, 1.2653, 1.2652, 1.2653],
[1502929062, 1.2631, 1.2631, 1.263, 1.2631],
[1502929063, 1.2625, 1.2625, 1.2625, 1.2625],
[1502929064, 1.2619, 1.2619, 1.2619, 1.2619],
[1502929065, 1.2622, 1.2623, 1.2622, 1.2623],
[1502929066, 1.2622, 1.2623, 1.2622, 1.2623],
[1502929067, 1.2617, 1.262, 1.2617, 1.262]
]
and I'm using the code blow to plot the candlesticks:
for row in ohlc:
row[0] = mdates.date2num(datetime.datetime.fromtimestamp(row[0]))
fig = plt.figure()
ax1 = plt.subplot2grid((1,1), (0,0))
candlestick_ohlc(ax1,ohlc,width=0.1)
fig.subplots_adjust(bottom=0.3)
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%y-%m-%d %H:%M:%S'))
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()
but the candlesticks being drawn on top of each other:
as I checked the code further, I noticed that mdates.date2num(datetime.datetime.fromtimestamp(row[0])) is actually generating dates with very minute differences (and therefore candlesticks being drawn on top of each other):
736558.1997453704
736558.1997569444
736558.1997685186
736558.1997800926
736558.1997916667
736558.1998032407
736558.1998148148
736558.1998263889
736558.199837963
736558.199849537
what is the solution to this problem?

Obtaining hyperpolarization depth from electrophysiological graph

I am working on electrophysiological data which is in .abf format.
I want to obtain the hyperpolarization depth as indicated above in the figure. This is what I have done so far;
import matplotlib.pyplot as plt
import pyabf
import pandas as pd
abf = pyabf.ABF("test.abf")
abf.setSweep(10) # I can access a given sweep. Here sweep 10
df = pd.DataFrame({'time': abf.sweepX, 'current':abf.sweepY})
df1 = df.loc[15650:15800]
df1.plot(x='time', y='current')
I am thinking to apply change in derivative to find the first point of interest (x1,y1) and then lower point (x2,y2), but it looks complex. I would appreciate if someone give some hint or procedure.
The dataset as follow,
time current
0.7825 -63.323975
0.78255 -63.171387
0.7826 -62.89673
0.78265 -62.713623
0.7827 -62.469482
0.78275 -62.37793
0.7828 -62.10327
0.78285 -61.950684
0.7829 -61.76758
0.78295 -61.584473
0.783 -61.401367
0.78305 -61.24878
0.7831 -61.035156
0.78315 -60.85205
0.7832 -60.72998
0.78325 -60.516357
0.7833 -60.455322
0.78335 -60.2417
0.7834 -60.08911
0.78345 -59.96704
0.7835 -59.814453
0.78355 -59.661865
0.7836 -59.509277
0.78365 -59.417725
0.7837 -59.23462
0.78375 -59.11255
0.7838 -58.95996
0.78385 -58.86841
0.7839 -58.685303
0.78395 -58.59375
0.784 -58.441162
0.78405 -58.34961
0.7841 -58.19702
0.78415 -58.044434
0.7842 -57.922363
0.78425 -57.769775
0.7843 -57.678223
0.78435 -57.434082
0.7844 -57.34253
0.78445 -56.9458
0.7845 -56.274414
0.78455 -54.96216
0.7846 -53.253174
0.78465 -51.208496
0.7847 -48.950195
0.78475 -46.325684
0.7848 -43.09082
0.78485 -38.42163
0.7849 -31.036377
0.78495 -22.033691
0.785 -13.397217
0.78505 -6.072998
0.7851 -0.61035156
0.78515 2.7160645
0.7852 3.9367676
0.78525 3.4179688
0.7853 1.3427734
0.78535 -1.4953613
0.7854 -5.0964355
0.78545 -9.185791
0.7855 -13.641357
0.78555 -18.249512
0.7856 -23.132324
0.78565 -27.98462
0.7857 -32.714844
0.78575 -37.261963
0.7858 -41.47339
0.78585 -45.22705
0.7859 -48.553467
0.78595 -51.54419
0.786 -53.985596
0.78605 -56.18286
0.7861 -58.013916
0.78615 -59.539795
0.7862 -60.760498
0.78625 -61.88965
0.7863 -62.652588
0.78635 -63.323975
0.7864 -63.934326
0.78645 -64.2395
0.7865 -64.60571
0.78655 -64.78882
0.7866 -65.00244
0.78665 -64.971924
0.7867 -65.093994
0.78675 -65.03296
0.7868 -64.971924
0.78685 -64.819336
0.7869 -64.78882
0.78695 -64.66675
0.787 -64.48364
0.78705 -64.42261
0.7871 -64.2395
0.78715 -64.11743
0.7872 -63.964844
0.78725 -63.842773
0.7873 -63.659668
0.78735 -63.568115
0.7874 -63.446045
0.78745 -63.26294
0.7875 -63.171387
0.78755 -62.98828
0.7876 -62.89673
0.78765 -62.74414
0.7877 -62.713623
0.78775 -62.530518
0.7878 -62.438965
0.78785 -62.37793
0.7879 -62.25586
0.78795 -62.164307
0.788 -62.042236
0.78805 -62.01172
0.7881 -61.88965
0.78815 -61.88965
0.7882 -61.73706
0.78825 -61.706543
0.7883 -61.645508
0.78835 -61.61499
0.7884 -61.523438
0.78845 -61.462402
0.7885 -61.431885
0.78855 -61.340332
0.7886 -61.37085
0.78865 -61.279297
0.7887 -61.279297
0.78875 -61.157227
0.7888 -61.187744
0.78885 -61.09619
0.7889 -61.157227
0.78895 -61.12671
0.789 -61.09619
0.78905 -61.12671
0.7891 -61.00464
0.78915 -61.00464
0.7892 -60.97412
0.78925 -60.97412
0.7893 -60.943604
0.78935 -61.00464
0.7894 -60.913086
0.78945 -60.97412
0.7895 -60.943604
0.78955 -60.913086
0.7896 -60.943604
0.78965 -60.85205
0.7897 -60.85205
0.78975 -60.821533
0.7898 -60.88257
0.78985 -60.88257
0.7899 -60.913086
0.78995 -60.88257
0.79 -60.913086

We can plot the difference in current between consecutive points (which essentially is to a constant factor the derivative, since times are evenly spaced). First chart shows the actual diffs. Based on this we can set some threshold, such as 0.3, and apply it to filter the main DataFrame. The filtered values are shown in orange on the second chart:
fig, ax = plt.subplots(2, figsize=(8,8))
# plot derivative
df['current'].diff().plot(ax=ax[0])
# current
threshold = 0.4
df['filtered'] = df.loc[df['current'].diff().abs() > threshold]
df.plot(ax=ax[1])
# add spans
x = df['filtered'].dropna()
ax[1].axhspan(x.iloc[0], x.iloc[-1], alpha=0.3, edgecolor='skyblue', facecolor="none", hatch='////')
ax[1].axvspan(x.index.min(), x.index.max(), alpha=0.3, edgecolor='orange', facecolor="none", hatch='\\\\')
Output:
If you're interested in range values, you can dropna values in the filtered subset and find min and max from the index:
print('min', df['filtered'].dropna().index.min())
print('max', df['filtered'].dropna().index.max())
Output:
min 0.78445
max 0.7865
For the value of the gap you can use:
abs(df['filtered'].dropna().iloc[-1] - df['filtered'].dropna().iloc[0])
Output:
7.6599100000000035
Note: We can alternatively also get left edges of these spans as points where diff in the point is lower than the threshold and diff in the next point is higher than the threshold, and similarly for the right edges. This would also work in case we have multiple peaks:
threshold = 0.3
x = df['current'].diff().abs()
spanA = df.loc[(x < threshold) & (x.shift(-1) >= threshold)]
spanB = df.loc[(x >= threshold) & (x.shift(-1) < threshold)]
print(spanA)
current
time
0.7844 -57.34253
print(spanB)
current
time
0.7865 -64.60571

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