This question already has answers here:
Using pandas crosstab to create a bar plot
(2 answers)
count plot with stacked bars per hue [duplicate]
(1 answer)
How to have clusters of stacked bars
(10 answers)
Closed 7 months ago.
I have a df like this and would like to plot stacked bar chart where in the x axis is Component and the y-axis shows the count by 'Major', 'Minor' etc.
Component Priority
0 Browse Groups Minor
1 Notifications Major
2 BI Major
3 BI Minor
4 BI Minor
For example, the first bar would have 1st component with a count of 1 minor,..so on.. and 3rd would have 'BI' in x-axis with 1 count of Major and 2 counts of Minor stacked.
What is the simplest way to do this in seaborn or something similar?
You can groupby both columns and count on Priority, then unstack and plot as stacked bar chart:
df.groupby(['Component', 'Priority']).Priority.count().unstack().plot.bar(stacked=True)
Example:
import pandas as pd
df = pd.DataFrame({'Component': list('abccc'), 'Priority': ['Minor', 'Major', 'Major', 'Minor', 'Minor']})
df.groupby(['Component', 'Priority']).Priority.count().unstack().plot.bar(stacked=True)
As an alternative, you can use a crosstab:
pd.crosstab(df.Component, df.Priority).plot.bar(stacked=True)
If you want to use seaborn (I only now saw the seaborn tag), you can use a displot:
import seaborn as sns
sns.displot(x='Component', hue='Priority', data=df, multiple='stack')
Related
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Closed 8 months ago.
I would like to create a subplot of bar chart where '% of total' is the y-axis and 'plants' is the x-axis. Also 'brand' will be legend, so in this case 3 different charts for the 3 different 'brands'. Each groups % adds up to 100%. I started with the code below, but got stuck. Please see a sample of the data below and image below;
import pandas as pd
import numpy as np
df = pd.DataFrame({
'brand':['A','A', 'A', 'B','B', 'B' ,'C','C', 'C'],
'plants':[0, 1, 2, 0,1,2,0,1,2],
'% of total':[80, 12, 8, 67, 18, 5,35, 40,25],
})
plt.figure(figsize=(10, 10))
for i, brand in enumerate(['A', 'B', 'C']):
You can use seaborn and catplot:
# Python env: pip install seaborn
# Anaconda env: conda install seaborn
import seaborn as sns
import matplotlib.pyplot as plt
sns.catplot(x='plants', y='% of total', col='brand', data=df, kind='bar')
plt.show()
Output:
Does this need to be in a for loop? You could simply grab the relevant rows using pandas.
For example:
my_A_df = df[df['brand'] == A]
plt.hist(my_A_df)
plt.bar(my_A_df['plants'], my_A_df['% of total'])
This will work for generating a barplot for each. Not sure if this is within the bounds of your problem but happy to edit if necessary.
This question already has an answer here:
Using pandas value_counts and matplotlib
(1 answer)
Closed 8 months ago.
I have a dataframe with several columns and I need to plot a graph based on the number of counts in the 'Total'column.
I performed the following code:
df['Total'].value_counts()
The output are as follows:
2 10
20 15
4 8
8 20
This means the the number 2 appears in the Total columns 10 times, number 20 appears 15 times and so on.
How do I plot a barchart with the x-axis as the number itself and the y-axis as the occurances and in ascending
order? The x-axis will plot 2 -> 4 -> 8 -> 20.
What are the next steps after:
%matplotlib inline
import matplotlib.pyplot as plt
Consider this as an example:
This denoted your 'Total' column -> [2,2,2,2,2,20,20,20,20,4,4,4,8,8,8,8,8]
import pandas as pd
import matplotlib.pyplot as plt
import collections
total = [2,2,2,2,2,20,20,20,20,4,4,4,8,8,8,8,8]
df = pd.DataFrame(total, columns=['total'])
#print(df.value_counts())
fig,ax = plt.subplots()
df['total'].value_counts().plot(ax = ax, kind = 'bar', ylabel = 'frequency')
plt.show()
This gives the following output:
I have the table with 5 columns with 8000 rows:
Market DeliveryWindowID #Orders #UniqueShoppersAvailable #UniqueShoppersFulfilled
NY 296 2 2 5
MA 365 3 4 8
How do I plot a graph in pandas or seaborn that will show the #Order, #UniqueShoppersAvailable, #UniqueShoppersFulfilled v/s the market and delivery window?
Using Seaborn, reshape your dataframe with melt first:
df_chart = df.melt(['Market','DeliveryWindowID'])
sns.barplot('Market', 'value',hue='variable', data=df_chart)
Output:
One way is to set Market as index forcing it onto the x axis and do a bar graph if you wanted a quick visualization. This can be stacked or not.
Not Stacked
import matplotlib .pyplot as plt
df.drop(columns=['DeliveryWindowID']).set_index(df.Market).plot(kind='bar')
Stacked
df.drop(columns=['DeliveryWindowID']).set_index(df.Market).plot(kind='bar', stacked=True)
This question already has answers here:
How to have clusters of stacked bars
(10 answers)
Closed 2 years ago.
I have a dataframe that looks like this:
df = pd.DataFrame(columns=["type", "App","Feature1", "Feature2","Feature3",
"Feature4","Feature5",
"Feature6","Feature7","Feature8"],
data=[["type1", "SHA",0,0,1,5,1,0,1,0],
["type2", "LHA",1,0,1,1,0,1,1,0],
["type2", "FRA",1,0,2,1,1,0,1,1],
["type1", "BRU",0,0,1,0,3,0,0,0],
["type2", "PAR",0,1,1,4,1,0,1,0],
["type2", "AER",0,0,1,1,0,1,1,0],
["type1", "SHE",0,0,0,1,0,0,1,0]])
I want to make a stacked bar with type as a hue. This is, in the x axis I want the features, and for each feature I want 2 stacked bars, one for type1 and one for type2.
For instance, here they explain how to make a stacked bar plot with seaborn when the column type is dropped. Instead, I want for each feature two stacked bars. Note: the values of App are shared for type1 and type2
For instance, if I just plot the stacked bars corresponding to type1, I get this:
I want to make a stacked bar plot where for each feature there are two stacked bars, one for type1, and the other one for type2
I don't think seaborn has a function for barplots that are both stacked and grouped. But you can do it in matplotlib itself by hand. Here is an example.
I think what you are looking for is the melt Function
d = df.drop(columns='App')
d = d.melt('type', var_name='a', value_name='b')
sns.barplot(x='a', y='b', data=d, hue='type')
I want to plot this data frame but I get an error.
this is my df:
6month final-formula Question Text
166047.0 1 0.007421 bathing
166049.0 1 0.006441 dressing
166214.0 1 0.001960 feeding
166216.0 2 0.011621 bathing
166218.0 2 0.003500 dressing
166220.0 2 0.019672 feeding
166224.0 3 0.012882 bathing
166226.0 3 0.013162 dressing
166229.0 3 0.008821 feeding
160243.0 4 0.023424 bathing
156876.0 4 0.000000 dressing
172110.0 4 0.032024 feeding
how can I plot a stacked bar based on the Question text?
I tried some codes but raises error.
dffinal.groupby(['6month','Question Text']).unstack('Question Text').plot(kind='bar',stacked=True,x='6month', y='final-formula')
import matplotlib.pyplot as plt
plt.show()
Actually I want the 6month column be in the x-axis, final-formula in the y-axis and Question text being stacked.
so as here I have three kind of Question text, three stacked bar should be there. and as I have 4 month, 4 bars totally.
Something like this but I applied this and did not work.
Am I missing something?
this picture is without stacking them. its like all question text has been summed up. I want for each Question Text there be stacked.
You missed aggregation step after groupby, namely, sum()
df = dffinal.groupby(['6month','Question Text']).sum().unstack('Question Text')
df.columns = df.columns.droplevel()
df.plot(kind='bar', stacked=True)
I dropped multiindex level from columns just for legend consistency.