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plostly histogram facet row animation frame

Here is a sample of my data:
Time,Value,Name,Type
0,6.9,A,start
40,6.9,A,start
60,6.9,A,start
0,0.01,B,start
40,0.01,B,start
60,0.01,B,start
0,1.0,C,start
40,1.0,C,start
60,1.0,C,start
0,0.08,D,start
40,0.08,D,start
60,0.08,D,start
0,0.000131,E,End
40,0.00032,E,End
60,0.99209,E,End
0,0.002754,F,End
40,0.00392,F,End
60,0.01857,F,End
0,0.003,G,End
40,0.00516,G,End
60,0.00746,G,End
0,0.00426,H,End
40,0.0043,H,End
60,0.0095,H,End
0,0,I,End
40,0.0017,I,End
60,0.0183,I,End
And my code below:
import plotly.express as px
import pandas as pd
df=pd.read_csv('tohistogram.csv')
fig_bar = px.histogram(df,x='Name',y='Value',animation_frame='Time',color='Name',facet_row='Type')
fig_bar.update_layout(yaxis_title="value")
fig_bar.update_xaxes(matches=None)
fig_bar.for_each_xaxis(lambda xaxis: xaxis.update(showticklabels=True))
fig_bar.show()
`
Fig1:
Fig2:
With the data point listed above, I wanted 2 histogram separated by type (start,end) in one frame with one animation_frame
Tried the above code, as one can see from the image I could partial achieve but from Fig1: second histogram has (A,B,C,D),excepted just E to I.
2. Figure 2 was when I played the run button and auto scaled then I see A-D are gone and only E-I,
This is what I wanted to achieve in the first place itself, before running 2 histogram should sort as per 'Type'
A. Is it possible I tried couple of things like removed color
fig_bar = px.histogram(df,x='Name',y='Value',animation_frame='Time',facet_row='Type')
histogram sorts as per 'Type' of course no color but no label in second x-axis.
B.fig_bar = px.histogram(df,x='Name',y='Value',color='Name',facet_row='Type')
It sorts but no animation
What I am trying is it possible?
need 2 histogram with in the same frame sorted by 'Type',color and animation_frame?
C. Only if possible then, how to label y-axis of the first histogram from sumofValues to user-defined axis name and also have its own axis range.
D.I didn't come across any example but on the histogram, on mouse hover can I show another simple line graph image instead of text or value?
Thank you

Matplotlib plot plotting the wrong data values

I am trying to plot random rows in a dataset, where the data consists of data collated across different dates. I have plotted it in such a way that the x-axis is labelled for the specific dates, and there is no interpolation between dates.
The issue I am having, is that the values plotted by matplotlib, do not match the entry values in the dataset. I am unsure as to what is happening here, would anyone be able to provide some insight, and possibly as to how I would fix it?
I have attached an image of the dataset and the plot, with the code contained below.
The code for generating the x-ticks, is as follows:
In: #creating a flat dates object such that dates are integer objects
flat_Dates_dates = flat_Dates[2:7]
flat_Dates_dates
Out: [20220620, 20220624, 20220627, 20220701, 20220708]
In: #creating datetime object(pandas, not datetime module) to only plot specific dates and remove interpolation of dates
date_obj_pd = pd.to_datetime(flat_Dates_dates, format=("%Y%m%d"))
Out: DatetimeIndex(['2022-06-20', '2022-06-24', '2022-06-27', '2022-07-01',
'2022-07-08'],
dtype='datetime64[ns]', freq=None)
As you can see from the dataset, the plotted trends should not take that form, the data values are wildly different from where they should be on the graph.
Edit: Apologies, I forgot to mention x = date_obj_pd - which is why I added the code, essentially just the array of datetime objects.
y is just the name of the pandas DataFrame (data table) I have included in the image.

You are plotting columns instead of rows. The blue line contains elements 1:7 from the first column, namely these:
If you transpose the dataframe you should get the desired result:
plt.plot(x, y[1:7].transpose(), 'o--')

how to add to plot gaps when observations are missed?

Here is what i got (time series) in pandas dataframe
screenshot
(also dates were converted from timestamps)
My goal is to plot not only observations, but all the range of dates. I need to see horizontal line or gap when there is no new observations.

Dealing with data that is not observed equidistant in time is a typical challenge with real-world time series data. Given your problem, this code should work.
from datetime import datetime
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
# sample Frame
df = pd.DataFrame({'time' : ['2022,7,3,0,1,21', '2022,7,3,0,2,47', '2022,7,3,0,2,47', '2022,7,3,0,5,5',
'2022,7,3,0,5,5'],
'balance' : [12.6, 12.54, 12.494426, 12.482481, 12.449206]})
df['time'] = pd.to_datetime(df['time'], format='%Y,%m,%d,%H,%M,%S')
# aggregate time duplicates by mean
df = df.groupby('time').mean()
df.reset_index(inplace=True)
# pick equidistant time grid
df_new = pd.DataFrame({'time' : pd.date_range(start=df.loc[0]['time'], end=df.loc[2]['time'], freq='S')})
df = pd.merge(left=df_new, right=df, on='time', how='left')
# fill nan
df['balance'].fillna(method='pad', inplace=True)
df.set_index("time", inplace=True)
# plot
_ = df.plot(title='Time Series of Balance')
There are several caveats to this solution.
First, your data has a high temporal resolution (seconds). However, there are hours-long gaps in between observations. You either coarsen the timestamp by rounding (e.g. to minutes or hours) or go along with the time series on a second-by-second resolution and accept the fact that most you balance values will be filled-in values rather than true observations.
Second, you have different balance values for the same timestamp which indicates faulty entries or a misspecified timestamp. I unified those entries via grouping by timestamp and averaged the balance over those non-unique timestamps.
Third, filled-up gaps and true observations both have the same visual representation in the plot (blue dots in the graph). As previously mentioned commenting out the fillna() line would only showcase true observations leaving everything in between white.
Finally, the missing values are merely filled in via padding. Look up different values of the argument method in the documentation in case you want to linearly interpolate etc.
Summary
The problems described above are typical for event-driven time series data. Since you deal with a (financial) balance that constitutes a state that is only changed by events (orders), I believe that the assumptions made above arew reasonable and can be adjusted easily for your or many other use cases.

this helped
data = data.set_index('time').resample('1M').mean()

Plots do not appear when calling seaborn's pairplot on a pandas Dataframe

I have a Dataframe that looks like so
Price Mileage Age
4250 71000 8
6500 43100 6
26950 10000 3
1295 78000 17
5999 61600 8
This is assigned to dataset. I simply call sns.pairplot(dataset) and I'm left with just a single graph - the distribution of prices across my dataset. I expected a 3x3 grid of plots.
When I import a pre-configured dataset from seaborn I get the expected multiplot pair plot.
I'm new to seaborn so apologies if this is a silly question, but what am I doing wrong? It seems like a simple task.

From your comment, it seems like you're trying to plot on non-numeric columns. Try coercing them first:
dataset = dataset.apply(lambda x: pd.to_numeric(x, errors='coerce'))
sns.pairplot(dataset)
The errors='coerce' argument will replace non-coercible values (the reason your columns are objects in the first place) to NaN.

Plotting boxplots for a groupby object

I would like to plot boxplots for several datasets based on a criterion.
Imagine a dataframe similar to the example below:
df = pd.DataFrame({'Group':[1,1,1,2,3,2,2,3,1,3],'M':np.random.rand(10),'F':np.random.rand(10)})
df = df[['Group','M','F']]
Group M F
0 1 0.465636 0.537723
1 1 0.560537 0.727238
2 1 0.268154 0.648927
3 2 0.722644 0.115550
4 3 0.586346 0.042896
5 2 0.562881 0.369686
6 2 0.395236 0.672477
7 3 0.577949 0.358801
8 1 0.764069 0.642724
9 3 0.731076 0.302369
In this case, I have three groups, so I would like to make a boxplot for each group and for M and F separately having the groups on Y axis and the columns of M and F colour-coded.
This answer is very close to what I want to achieve, but I would prefer something more robust, applicable for larger dataframes with greater number of groups. I feel that groupby is the way to go, but I am not familiar with groupby objects and I am failing to even slice them.
. The desirable output would look something like this:
Looks like years ago, someone had the same problem, but got no answers :( Having a boxplot as a graphical representation of the describe function of groupby
My questions are:
How to implement groupby to feed the desired data into the boxplot
What is the correct syntax for the box plot if I want to control what is displayed and not just use default settings (which I don't even know what they are, I am finding the documentation rather vague. To be specific,can I have the box covering the mean +/- standard deviation, and keep the vertical line at median value?)

I think you should use Seaborn library that offers to create these type of customize plots.In your case i had first melted your dataframe to convert it into proper format and then created the boxplot of your choice.
import pandas as pd
import matplotlib.pyplot as plt
Import seaborn as sns
dd=pd.melt(df,id_vars=['Group'],value_vars=['M','F'],var_name='sex')
sns.boxplot(y='Group',x='value',data=dd,orient="h",hue='sex')
The plot looks similar to your required plot.

Finally, I found a solution by slightly modifying this answer. It does not use groupby object, so it is more tedious to prepare the data, but so far it looks like the best solution to me. Here it is:
# here I prepare the data (group them manually and then store in lists)
Groups=[1,2,3]
Columns=df.columns.tolist()[1:]
print Columns
Mgroups=[]
Fgroups=[]
for g in Groups:
dfgc = df[df['Group']==g]
m=dfgc['M'].dropna()
f=dfgc['F'].dropna()
Mgroups.append(m.tolist())
Fgroups.append(f.tolist())
fig=plt.figure()
ax = plt.axes()
def setBoxColors(bp,cl):
plt.setp(bp['boxes'], color=cl, linewidth=2.)
plt.setp(bp['whiskers'], color=cl, linewidth=2.5)
plt.setp(bp['caps'], color=cl,linewidth=2)
plt.setp(bp['medians'], color=cl, linewidth=3.5)
bpl = plt.boxplot(Mgroups, positions=np.array(xrange(len(Mgroups)))*3.0-0.4,vert=False,whis='range', sym='', widths=0.6)
bpr = plt.boxplot(Fgroups, positions=np.array(xrange(len(Fgroups)))*3.0+0.4,vert=False,whis='range', sym='', widths=0.6)
setBoxColors(bpr, '#D7191C') # colors are from http://colorbrewer2.org/
setBoxColors(bpl, '#2C7BB6')
# draw temporary red and blue lines and use them to create a legend
plt.plot([], c='#D7191C', label='F')
plt.plot([], c='#2C7BB6', label='M')
plt.legend()
plt.yticks(xrange(0, len(Groups) * 3, 3), Groups)
plt.ylim(-3, len(Groups)*3)
#plt.xlim(0, 8)
plt.show()
The result looks mostly like what I wanted (as far as I have been able to find, the box always ranges from first to third quartile, so it is not possible to set it to +/- standard deviation). So I am a bit disappointed there is no one-line solution, but I am glad it is possible. However, for hundreds of groups this would not be good enough...