We Keep Coding

Create Multiple Subplots of sns.factorplot based on Dataframe Integer Column Values

I have a dataframe like on below,
df_sales:
ProductCode Weekly_Units_Sold Is_Promo
Date
2015-01-11 1 49.0 No
2015-01-11 2 35.0 No
2015-01-11 3 33.0 No
2015-01-11 4 40.0 No
2015-01-11 5 53.0 No
... ... ... ...
2015-07-26 313 93.0 No
2015-07-26 314 4.0 No
2015-07-26 315 1.0 No
2015-07-26 316 5.0 No
2015-07-26 317 2.0 No
Want to observe Promotime effect on Each ProductCode with Sns.factorplot like code on below:
sns.factorplot(data= df_sales,
x= 'Is_Promo',
y= 'Weekly_Units_Sold',
hue= 'ProductCode');
It is working good but it seems very confusing and overlapped due to 317 product plotted in one table.(https://i.stack.imgur.com/fgrjV.png)
When i split the dataframe with this code:
df_sales=df_sales.query('1<=ProductCode<=10')
It looks great readbility.
https://i.stack.imgur.com/NTQev.png
So, ı wanted to draw as subplots with help of splitting data respect of 10 productcode range(like first subplot ProductCOde is [1-10], second[11-20]..[291-300],[301-310],[311-317] in each subplot.
My Failed Tries :`
g=sns.FacetGrid(df_sales,col='ProductCode')
g.map(sns.factorplot,'Is_Promo','Weekly_Units_Sold')
sns.factorplot(data= df_sales,
x= 'Is_Promo',
y= 'Weekly_Units_Sold',
hue= 'ProductCode');
I tried not splitting with 10 ProductCode ranges.
I have just tried to create subplot for each ProductCode but
gave me image size error.
So how can I create subplots of sns.factorplot splitted respect to ProductCode range to get more readible results?
Thanks

You need to create a new column with a unique value for each group of products. A simple way of doing that is using pd.cut()
Nproducts = 100
Ngroups = 10
df1 = pd.DataFrame({'ProductCode':np.arange(Nproducts),
'Weekly_Units_Sold': np.random.random(size=Nproducts),
'Is_Promo':'No'})
df2 = pd.DataFrame({'ProductCode':np.arange(Nproducts),
'Weekly_Units_Sold': np.random.random(size=Nproducts),
'Is_Promo':'Yes'})
df = pd.concat([df1,df2])
df['ProductGroup'] = pd.cut(df['ProductCode'], Ngroups, labels=False)
After that, you can facet based on ProductGroup, and plot whatever relationship you want for each group.
g = sns.FacetGrid(data=df, col='ProductGroup', col_wrap=3, hue='ProductCode')
g.map(sns.pointplot, 'Is_Promo', 'Weekly_Units_Sold', order=['No','Yes'])
Note that this using seaborn v.0.10.0. factorplot() was renamed catplot in v.0.9 so you may have to adjust for version difference.
EDIT: to create a legend, I had to modify a bit the code to move the hue parameter out of the FacetGrid:
g = sns.FacetGrid(data=df, col='ProductGroup', col_wrap=3)
g.map_dataframe(sns.pointplot, 'Is_Promo', 'Weekly_Units_Sold', order=['Yes','No'], hue='ProductCode')
for ax in g.axes.ravel():
ax.legend(loc=1, bbox_to_anchor=(1.1,1))

How to make a categorical barplot with time series in Bokeh?

I'd like to make a categorical barplot with timeseries on the x-axis.
My dataframe looks like this:
VRI TIME QTY
0 308 00:00:00 613.0
1 308 00:15:00 581.0
...
92 309 00:00:00 299.0
93 309 00:15:00 300.5
...
188 310 00:00:00 166.0
189 310 00:15:00 125.0
...
284 328 00:00:00 133.5
285 328 00:15:00 85.5
The VRI needs to be the categorical variable, so I'd like to create 4 bargraphs next to each other.
On the X-axis I would like to have the TIME column, which consists of all the hours of a day per 15 minutes.
This is what my code looks like right now:
source = ColumnDataSource(vri_data)
p = figure(x_axis_type='datetime', title='Total traffic intensity per VRI', plot_width=1000)
p.vbar(x='time',top='aantal', width=timedelta(minutes=10), source=source, hover_line_color="black")
p.xaxis.axis_label = 'Time'
p.yaxis.axis_label = 'Traffic intensity'
hover = HoverTool(tooltips=
[
('Time', '#time'),
('Traffic Intensity', '#aantal'),
('VRI Number', '#vri')
])
p.add_tools(hover)
show(p)
It outputs this:
In this plot all the 4 graphs are placed on top of each other, making some invisible. Now what I would like is to have 4 bargraphs next to each other instead of on top of each other, one for every distinct VRI value.
I have tried to use:
p = figure(x_range = vri_data['vri'], ...
But this outputs ValueError: Unrecognized range input:
Does anyone know a fix in order to get the plot as I want it?
Thanks!

There are two options:
Turn the X axis to a proper categorical one, making each of those 15 minutes intervals a separate categories. That would allow you to use nested categories as described here in the Bokeh documentation.
Do it all manually. Either add a color column to the data source and use specify the corresponding vbar parameter or just create 4 vbars, 1 for each VRI value.

Change tick frequency for datetime axis [duplicate]

This question already has an answer here:
Change tick frequency on X (time, not number) frequency in matplotlib
(1 answer)
Closed 3 years ago.
I have the following dataframe:
Date Prod_01 Prod_02
19 2018-03-01 49870 0.0
20 2018-04-01 47397 0.0
21 2018-05-01 53752 0.0
22 2018-06-01 47111 0.0
23 2018-07-01 53581 0.0
24 2018-08-01 55692 0.0
25 2018-09-01 51886 0.0
26 2018-10-01 56963 0.0
27 2018-11-01 56732 0.0
28 2018-12-01 59196 0.0
29 2019-01-01 57221 5.0
30 2019-02-01 55495 472.0
31 2019-03-01 65394 753.0
32 2019-04-01 59030 1174.0
33 2019-05-01 64466 2793.0
34 2019-06-01 58471 4413.0
35 2019-07-01 64785 6110.0
36 2019-08-01 63774 8360.0
37 2019-09-01 64324 9558.0
38 2019-10-01 65733 11050.0
And I need to plot a time series of the 'Prod_01' column.
The 'Date' column is in the pandas datetime format.
So I used the following command:
plt.figure(figsize=(10,4))
plt.plot('Date', 'Prod_01', data=test, linewidth=2, color='steelblue')
plt.xticks(rotation=45, horizontalalignment='right');
Output:
However, I want to change the frequency of the xticks to one month, so I get one tick and one label for each month.
I have tried the following command:
plt.figure(figsize=(10,4))
plt.plot('Date', 'Prod_01', data=test, linewidth=2, color='steelblue')
plt.xticks(np.arange(1, len(test), 1), test['Date'] ,rotation=45, horizontalalignment='right');
But I get this:
How can I solve this problem?
Thanks in advance.

I'm not very familiar with pandas data frames. However, I can't see why this wouldn't work with any pyplot:
According the top SO answer on related post by ImportanceOfBeingErnest:
The spacing between ticklabels is exclusively determined by the space between ticks on the axes.
So, to change the distance between ticks, and the labels you can do this:
Suppose a cluttered and base-10 centered person displays the following graph:
It takes the following code and importing matplotlib.ticker:
import numpy as np
import matplotlib.pyplot as plt
# Import this, too
import matplotlib.ticker as ticker
# Arbitrary graph with x-axis = [-32..32]
x = np.linspace(-32, 32, 1024)
y = np.sinc(x)
# -------------------- Look Here --------------------
# Access plot's axes
axs = plt.axes()
# Set distance between major ticks (which always have labels)
axs.xaxis.set_major_locator(ticker.MultipleLocator(5))
# Sets distance between minor ticks (which don't have labels)
axs.xaxis.set_minor_locator(ticker.MultipleLocator(1))
# -----------------------------------------------------
# Plot and show graph
plt.plot(x, y)
plt.show()
To change where the labels are placed, you can change the distance between the 'major ticks'. You can also change the smaller 'minor ticks' in between, which don't have a number attached. E.g., on a clock, the hour ticks have numbers on them and are larger (major ticks) with smaller, unlabeled ones between marking the minutes (minor ticks).
By changing the --- Look Here --- part to:
# -------------------- Look Here --------------------
# Access plot's axes
axs = plt.axes()
# Set distance between major ticks (which always have labels)
axs.xaxis.set_major_locator(ticker.MultipleLocator(8))
# Sets distance between minor ticks (which don't have labels)
axs.xaxis.set_minor_locator(ticker.MultipleLocator(4))
# -----------------------------------------------------
You can generate the cleaner and more elegant graph below:
Hope that helps!

Pandas: Histogram Plotting

I have a dataframe with dates (datetime) in python. How can I plot a histogram with 30 min bins from the occurrences using this dataframe?
starttime
1 2016-09-11 00:24:24
2 2016-08-28 00:24:24
3 2016-07-31 05:48:31
4 2016-09-11 00:23:14
5 2016-08-21 00:55:23
6 2016-08-21 01:17:31
.............
989872 2016-10-29 17:31:33
989877 2016-10-02 10:00:35
989878 2016-10-29 16:42:41
989888 2016-10-09 07:43:27
989889 2016-10-09 07:42:59
989890 2016-11-05 14:30:59
I have tried looking at examples from Plotting series histogram in Pandas and A per-hour histogram of datetime using Pandas. But they seem to be using a bar plot which is not what I need. I have attempted to create the histogram using temp.groupby([temp["starttime"].dt.hour, temp["starttime"].dt.minute]).count().plot(kind="hist") giving me the results as shown below
If possible I would like the X axis to display the time(e.g 07:30:00)

I think you need bar plot and for axis with times simpliest is convert datetimes to strings by strftime:
temp = temp.resample('30T', on='starttime').count()
ax = temp.groupby(temp.index.strftime('%H:%M')).sum().plot(kind="bar")
#for nicer bar some ticklabels are hidden
spacing = 2
visible = ax.xaxis.get_ticklabels()[::spacing]
for label in ax.xaxis.get_ticklabels():
if label not in visible:
label.set_visible(False)

Plot datetime.date / time series in a pandas dataframe

I created a pandas dataframe from some value counts on particular calendar dates. Here is how I did it:
time_series = pd.DataFrame(df['Operation Date'].value_counts().reset_index())
time_series.columns = ['date', 'count']
Basically, it is two columns, the first "date" is a column with datetime.date objects and the second column, "count" are simply integer values. Now, I'd like to plot a scatter or a KDE to represent how the value changes over the calendar days.
But when I try:
time_series.plot(kind='kde')
plt.show()
I get a plot where the x-axis is from -50 to 150 as if it is parsing the datetime.date objects as integers somehow. Also, it is yielding two identical plots rather than just one.
Any idea how I can plot them and see the calendars day along the x-axis?

you sure you got datetime? i just tried this and it worked fine:
df = date count
7 2012-06-11 16:51:32 1.0
3 2012-09-28 08:05:14 12.0
19 2012-10-01 18:01:47 4.0
2 2012-10-03 15:18:23 29.0
6 2012-12-22 19:50:43 4.0
1 2013-02-19 19:54:03 28.0
9 2013-02-28 16:08:40 17.0
12 2013-03-12 08:42:55 6.0
4 2013-04-04 05:27:27 6.0
17 2013-04-18 09:40:37 29.0
11 2013-05-17 16:34:51 22.0
5 2013-07-07 14:32:59 16.0
14 2013-10-22 06:56:29 13.0
13 2014-01-16 23:08:46 20.0
15 2014-02-25 00:49:26 10.0
18 2014-03-19 15:58:38 25.0
0 2014-03-31 05:53:28 16.0
16 2014-04-01 09:59:32 27.0
8 2014-04-27 12:07:41 17.0
10 2014-09-20 04:42:39 21.0
df = df.sort_values('date', ascending=True)
plt.plot(df['date'], df['count'])
plt.xticks(rotation='vertical')
EDIT:
if you want a scatter plot you can:
plt.plot(df['date'], df['count'], '*')
plt.xticks(rotation='vertical')

If the column is datetime dtype (not object), then you can call plot() directly on the dataframe. You don't need to sort by date either, it's done behind the scenes if x-axis is datetime.
df['date'] = pd.to_datetime(df['date'])
df.plot(x='date', y='count', kind='scatter', rot='vertical');
You can also pass many arguments to make the plot nicer (add titles, change figsize and fontsize, rotate ticklabels, set subplots axis etc.) See the docs for full list of possible arguments.
df.plot(x='date', y='count', kind='line', rot=45, legend=None,
title='Count across time', xlabel='', fontsize=10, figsize=(12,4));
You can even use another column to color scatter plots. In the example below, the months are used to assign color. Tip: To get the full list of possible colormaps, pass any gibberish string to colormap and the error message will show you the full list.
df.plot(x='date', y='count', kind='scatter', rot=90, c=df['date'].dt.month, colormap='tab20', sharex=False);