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Pandas + Seaborn : compute number of 0 regarding categorical datas

I'm currently struggling with my dataframe in Pandas (new to this).
I have a 3 columns dataframe : Categorical_data1, Categorical_data2,Output. (2400 rows x 3 columns).
Both categorical data (inputs) are strings and output is depending of inputs.
Categorical_data1 = ['type1','type2', ... , 'type6']
Categorical_data2 = ['rain1','rain2', 'rain3','rain4]
So 24 possible pairs of categorical data.
I want to plot a heatmap (using seaborn for instance) of the number of 0 in outputs regarding couples of categorical data (Cat_data1,Cat_data2). I tried several things using boolean.
I tried to figure out how to compute exact amount of 0
count = ((df['Output'] == 0) & (df(['Categorical_Data1'] == 'type1') & (df(['Categorical_Data2'] == 'rain1')))).sum()
but it failed.
The output belongs to [0,1] with a large amount of 0 (around 1200 over 2400). My goal is to have something like this Source by jcdoming (I can't upload images...) with months = Categorical Data1, years = Categorical Data2 ; and numbers of 0 in ouputs).
Thank you for your help.

Use a seaborn countplot. It gives counts of categorical data occurrences in a certain feature. Use hue to add in the second feature to the visualization:
import seaborn as sns
sns.countplot(data=dataframe, x='Categorical_Data1', hue='Categorical_Data2')

Pandas plot several df with different variables on the same barplot

I have 4 Dataframes with different location: Indonesia, Singapore, Malaysia and Total each of them containing the percentage of the 5 top revenue-generating products. I have plotted them separately.
I want to combine them together on one plot where X-axis shows different locations and top-revenue-generating products for each location.
I have printed data frames and as you can see they have different products in them.
print(Ind_top_cat, Sin_top_cat, Mal_top_cat, Tot_top_cat)
Category Amt
M020P 0.144131
MH 0.099439
ML 0.055052
PB 0.050057
PPDR 0.048315
Category Amt
ML 0.480781
M015 0.073034
PPDR 0.035412
M025 0.033418
M020 0.031836
Category Amt
TN 0.343650
PPDR 0.190773
NMCN 0.118425
M015 0.047539
NN 0.038140
Category Amt
M020P 0.158575
MH 0.092012
ML 0.064179
PPDR 0.050803
PB 0.044301
Thanks to joelostblom I was able to construct a plot, however, there are still some issues.
enter image description here
all_countries = pd.concat([Ind_top_cat, Sin_top_cat, Mal_top_cat, Tot_top_cat])
all_countries['Category'] = all_countries.index
sns.barplot(x='Country', y='Amt',hue = 'Category',data=all_countries)
Is there any way I can put legend values on the x-axis (no need to colour categories on I want to instead colour countries), and put data values on top of bars. Also, bars are not centred and have no idea how to solve it.

You could create a new column in each dataframe with the country name, e.g.
Ind_top_cat['Country'] = 'Indonesia'
Sin_top_cat['Country'] = 'Singapore'
The you can create one big dataframe by concatenating the country dataframes together:
all_countries = pd.concat([Ind_top_cat, Sin_top_cat])
And finally, you can use a high level plotting library such as seaborn to assign one column to the x-axis location and one to the color of the bars:
import seaborn as sns
sns.barplot(x='Country', y='Amt', color='Category', data=all_countries)
You can scroll down to the second example on this page to get an idea what such a plot would look like (also pasted below):

A line graph for non-numeric data

I have a dataset with mostly non numeric forms. I would love to create a visualization for them but I am having an error message.
My data set looks like this
|plant_name|Customer_name|Job site|Delivery.Date|DeliveryQuantity|
|SN13|John|Sweden|01.01.2019|6|
|SN14|Ruth|France|01.04.2018|4|
|SN15|Jane|Serbia|01.01.2019|2|
|SN11|Rome|Denmark|01.04.2018|10|
|SN14|John|Sweden|03.04.2018|5|
|SN15|John|Sweden|04.09.2019|7|
|
I need to create a lineplot to show how many times John made a purchase using Delivery Date as my timeline (x-axis)
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
pd.set_option("display.max_rows", 5)
hr_data = pd.read_excel("D:\data\Days_Calculation.xlsx", parse_dates = True)
x = hr_data['DeliveryDate']
y = hr_data ['Customer_name']
sns.lineplot(x,y)
Error: No numeric types to aggregate
My expected result show be a line graph like this
John's marker will present on the timeline (Delivery Date) on "01.01.2019", "03.04.2018" and "04.09.2019"
Another instance
To plot string vs float for example Total number of quantity (DeliveryQuantity) vs Customer Name .How can one approach this
how do one format the axes distance of a plot (not label)

Why not make Delivery Date a timestamp object instead of a string?
hr_data["Delivery.Date"] = pd.to_datetime(hr_data["Delivery.Date"])
Now you got plot options.
Working with John.
john_data = hr_data[hr_data["Customer_name"]=="John"]
sns.countplot(john_data["Delivery.Date"])

Generally speaking you have to aggregate something when working with categorical data. Whether you will be counting names in a column or adding number of orders, or ranking some categories this is still numeric data.
plot_data = hr_data.pivot_table(index='DeliveryDate', columns='Customer_name', values='DeliveryQuantity', aggfunc='sum')
plt.xticks(LISTOFVALUESFORXRANGE)
plot_data.plot(legend=False)

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.

Python scatterplot design - select specific values of a variable for the x axis based on another columns values

I am relatively new to python and am currently trying to generate a scatterplot based off of some data using pandas & seaborn.
The data I'm using ('ErrorMedianScatter') is as follows (apologies for the link, I have yet to get permissions to embed images!):
Image of data
Each participant has two data points of interest. The mean when MissingLimb = 0 or 1
I want to create a scatterplot for participants where the x-axis represents their value for 'mean' when 'MissingLimb' = 0, and the y-axis represents their value for 'mean' when 'MissingLimb' = 1.
I am using the current code so far to create a scatterplot:
sns.lmplot(("mean",
"mean",
data=ErrorMedianScatter,
fit_reg=False,
hue="participant")
This generates a perfectly functional, but very uninteresting, scatterplot. What I'm stuck on is creating an x-/y-axis variable that allows for me to specify that I'm interested in the mean of a participant based on the value of 'MissingLimb' column.
Many thanks in advance!

There are most likely multiple ways to solve your problem. The method I'd take is to first transform you dataset in such a way that there is a single row (observation) for each participant, and where (for each row) there is one column that reports the means where MissingLimb is 0 and another column that reports the means where MissingLimb is 1.
You can accomplish this data transformation with this code:
df = pd.pivot_table(ErrorMedianScatter,
values='mean',
index='participant',
columns='MissingLimb')
df.columns = ['MissingLimb 0', 'MissingLimb 1']
You can then use this (transformed) dataframe to create the scatterplot:
sns.lmplot(data=df, x='MissingLimb 0', y='MissingLimb 1')
Notice that in addition to specifying the data to plot (using the data parameter), I also specified the data to plot on the x- and y-axis (using the x and y parameters, respectively). You can add additional arguments to the sns.lmplot call and customize the plot to your specifications.