I am trying to plot a heatmap with 2 columns of data from a pandas dataframe. However, I would like to use a 3rd column to label the x axis, ideally by colour though another method such as an additional axis would be equally suitable. My dataframe is:
MUT SAMPLE VAR GROUP
True s1 1_1334442_T CC002
True s2 1_1334442_T CC006
True s1 1_1480354_GAC CC002
True s2 1_1480355_C CC006
True s2 1_1653038_C CC006
True s3 1_1730932_G CC002
...
Just to give a better idea of the data; there are 9 different types of 'GROUP', ~60,000 types of 'VAR' and 540 'SAMPLE's. I am not sure if this is the best way to build a heatmap in python but here is what I figured out so far:
pivot = pd.crosstab(df_all['VAR'],df_all['SAMPLE'])
sns.set(font_scale=0.4)
g = sns.clustermap(pivot, row_cluster=False, yticklabels=False, linewidths=0.1, cmap="YlGnBu", cbar=False)
plt.show()
I am not sure how to get 'GROUP' to display along the x-axis, either as an additional axis or just colouring the axis labels? Any help would be much appreciated.
I'm not sure if the 'MUT' column being a boolean variable is an issue here, df_all is 'TRUE' on every 'VAR' but as pivot is made, any samples which do not have a particular 'VAR' are filled as 0, others are filled with 1. My aim was to try and cluster samples with similar 'VAR' profiles. I hope this helps.
Please let me know if I can clarify anything further? Many thanks
Take look at this example. You can give a list or a dataframe column to the clustermap function. By specifying either the col_colors argument or the row_colors argument you can give colours to either the rows or the columns based on that list.
In the example below I use the iris dataset and make a pandas series object that specifies which colour the specific row should have. That pandas series is given as an argument for row_colors.
iris = sns.load_dataset("iris")
species = iris.pop("species")
lut = dict(zip(species.unique(), "rbg"))
row_colors = species.map(lut)
g = sns.clustermap(iris, row_colors=row_colors,row_cluster=False)
This code results in the following image.
You may need to tweak a bit further to also include a legend for the colouring for groups.
Related
I'm working on a project and would like to plot by data in a similar way as this example from a book:
So I would like to create a density histogram for my categorical features (left image) and than add a separate column for each value of another feature (middle and right image).
In my case the feature I want to plot is called [district_code] and I would like to create columns based on a feature called [status_group]
What I've tried so far:
sns.kdeplot(data = raw, x = "district_code"): problem, it is a line plot, not a histogram
sns.kdeplot(data = raw, x = "district_code", col = "status_group"): problem, you can't use the col argument for this plottype
sns.displot(raw, x="district_code", col = 'status_group'): problem, col argument works, but it creates a countplot, not a density plot
I would really appreciate some suggestions about the correct code I could use.
This is just an example for one of my categorical features, but I have many more I would like to plot. Any suggestions on how to turn this into a function where I could run the code for a list of categorical features would be highly appreciated.
UPDATE:
sns.displot(raw, x="source_class", stat = 'density', col = 'status_group', color = 'black'): works but looks a bit akward for some features.
How could I improve this?
Good:
Not so good:
I have survey dataset about different age of people over using various social media platform. I want to calculate the average number of people over social media app usage. Here is how example data looks like:
here is reproducible pandas dataframe:
df=pd.DataFrame({'age': np.random.randint(10,100,size=10),
'web1a': np.random.choice([1, 2], size=(10,)),
'web1b': np.random.choice([1, 2], size=(10,), p=[1./3, 2./3]),
'web1c': np.random.choice([1, 2], size=(10,)),
'web1d': np.random.choice([1, 2], size=(10,))})
here is what I tried:
df.pivot_table(df, values='web1a', index='age', aggfunc='mean')
but it is not efficient and didn't produce my desired output. Any idea to get this done? Thanks
update:
for me, the way to do this, first select categorical values in each column and get mean for it which can be the same for others. If I do that, how can I nicely plot them?
Note that in column web1a,web1b, web1c, web1d, 1 mean user and 2 means non-user respectively. I want to compute the average age of the user and non-user. How can I do that? Anyone give me a possible idea to make this happen? Thanks!
Using
df.melt('age').set_index(['variable','value']).mean(level=[0,1]).unstack().plot(kind='bar')
This can be done using groupby method:
df.groupby(['web1a', 'web1b', 'web1c', 'web1d']).mean()
You can groupby the 'web*' columns and calculate the mean on the 'age' column.
You can also plot bar charts (colors can be defined in the subplot). I'm not sure pie charts make sense in this case.
I tried with your data, taking only the columns starting with 'web'. There are more values than '1's and '2's, So I assumed you only wanted to analyze the users and non-users and nothing else. You can change the values or add other values in the chart in the same way, as long as you know what values you want to draw.
df = df.filter(regex=('web|age'),axis=1)
userNr = '1'
nonUserNr = '2'
users = list()
nonUsers = list()
labels = [x for x in df.columns.tolist() if 'web' in x]
for col in labels:
users.append(df.loc[:,['age',col]].groupby(col).mean().loc[userNr][0])
nonUsers.append(df.loc[:,['age',col]].groupby(col).mean().loc[nonUserNr][0])
from matplotlib import pyplot as plt
x = np.arange(1, len(labels)+1)
ax = plt.subplot(111)
ax.bar(x-0.1, users, width=0.2,color='g')
ax.bar(x+0.1,nonUsers, width=0.2,color='r')
plt.xticks(x, labels)
plt.legend(['users','non-users'])
plt.show()
df.melt(id_vars='age').groupby(['variable', 'value']).mean()
The goal is to plot the data frame I'm working with on a single chart, with a line for each value of init_population where the y-axis is count and x-axis is tick_number.
I've figured out how to use groupby() and plot() together to make this:
As you can see, all the lines are there nicely, but I'm pretty confident that the blue at the top that doesn't follow the relationship the other lines are following is actually a different column of data.
So that this is reproducible, the data is available here.
import pandas as pd
max_runs_data = pd.read_csv('clean_table.csv')
del max_runs_data['visualization']
max_runs_data.columns = ['run_number','init_population', 'tick', 'turtle_count']
max_runs_data.set_index('tick', inplace = True)
test_plot_1 = max_runs_data.groupby('init_population')['turtle_count'].plot()
test_plot_2 = max_runs_data.groupby('init_population').plot(y='turtle_count')
test_plot_1 is the linked image, test_plot_2 is a separate plot for each group.
Is it obvious how to specify the columns for x and y without losing the grouping on a single chart?
Thanks
I am trying to plot a 3D plot in Matplotlib from a Pointcloud data which is essentially extracted from two different classes.
However, I cannot differentiate the classes into different colors. My code is below.
x=pd.DataFrame(np.array(x).reshape(-1,1))
y=pd.DataFrame( np.array(y).reshape( -1, 1 ) )
z=pd.DataFrame(np.array(z).reshape(-1,1))
target=pd.DataFrame(np.array(target).reshape(-1,1))
new_data=[x,y,z,target]
new_data = pd.concat(new_data, axis=1, ignore_index=True )
new_data.columns = ['x','y','z','target']
colors=[]
fig=plt.figure(figsize=(8,8))
ax=fig.add_subplot(111,projection='3d')
ax.scatter(new_data.x,new_data.y,new_data.z,color='target')
The color argument cannot be linked to the class in the "Target" column in my dataframe. Is there something that I am missing?
I found the answer myself- Mapped the Dataframe to the arguments of Color using below col=new_data['target'].map({'Variable1':'r','Variable2 ':'g','Variable3':'b'})
you're saying that the colors should come from the values of the string 'target'. Change it to c=new_data.target
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.