Is there a way to make a scatterplot in using seaborn where the (x, y) values are paired? I'd like the x-axis to represent values under condition A, and the y-axis to represent values under condition B. Concretely, suppose that x-axis is patient weight before treatment and y-axis is patient weight after treatment. My data is formatted like the following:
df = pd.DataFrame(
{'n': [1, 2, 3, 1, 2, 3],
'treatment': ['before', 'before', 'before', 'after', 'after', 'after'],
'weight': np.random.rand(6)})
n treatment weight
0 1 before 0.431438
1 2 before 0.053631
2 3 before 0.567058
3 1 after 0.324254
4 2 after 0.624151
5 3 after 0.519498
I think this qualifies as tidy data but the single variable I want to plot is weight. All the documentation I see for seaborn shows examples for paired data like plotting variable x of each item against variable y of each item. For example, sepal_length versus sepal_width. But how might I plot x versus y where my x and y are coming from the same column?
Is the solution to reformat my data so that I have a weight_before and a weight_after column? If so, can you provide the cleanest way to modify the data using pandas? I know I can do something like the following, but I feel like it's not great syntax.
df['weight_before'] = df['weight']
df.loc[df['treatment'] != 'before', 'weight_before'] = np.nan
# and similar for df['weight_after']
If I understand you question correctly, this might work for you:
sns.lmplot(data=df.pivot(index='n', columns='treatment', values='weight'),
x='before', y='after', fit_reg=False)
Another way of doing it
Pivot the dataframe;
df2=pd.pivot_table(df, index='n',columns='treatment', values='weight', aggfunc=np.sum)
df2.reset_index(drop=True, inplace=True)
Plot Scatter
ax = sns.scatterplot(x="before", y="after",data=df2)
Chained solution
ax = sns.scatterplot(x="before", y="after",data=pd.pivot_table(df, index='n',columns='treatment', values='weight', aggfunc=np.sum).reset_index(drop=True))
Related
CSV1only is a dataframe uploaded from a CSV
Let CSV1only as a dataframe be a column such that:
TRADINGITEMID:
1233
2455
3123
1235
5098
as a small example
How can I plot a scatterplot accordingly, specifically the y-axis?
I tried:
import pandas as pd
import matplotlib.pyplot as plt
CSV1only.plot(kind='scatter',x='TRADINGITEMID', y= [1,2], color='b')
plt.xlabel('TRADINGITEMID Numbers')
plt.ylabel('Range')
plt.title('Distribution of ItemIDNumbers')
and it doesn't work because of the y.
So, my main question is just how I can get a 0, 1, 2 y-axis for this scatter plot, as I want to make a distribution graph.
The following code doesn't work because it doesn't match the amount of rows included in the original TRADINGITEMID column, which has 5000 rows:
newcolumn_values = [1, 2]
CSV1only['un et deux'] = newcolumn_values
#and then I changed the y = [1,2] from before into y = ['un et deux']
Therefore the solution would need to work from any integer 1 to N, N being the # of rows. Yet, it would only have a range of [0, 2] or some [0, m], m being some arbitrary integer.
Don't need to worry about the actual pandas data frame CSV1only.
The 'TRADINGITEMIDNUMBERS' contains 5000 rows of unique numbers, so I just wanna plot those numbers on a line, with the y-axis being instances (which will never pass 1 since it is unique).
I think you are looking for the following: You need to generate y-values starting from 0 until n-1 where n is the total number of rows
y = np.arange(len(CSV1only['TRADINGITEMID']))
plt.scatter(CSV1only['TRADINGITEMID'], y, c='DarkBlue')
I have data of factories and their error codes during production
such as below;
PlantID A B C D
1 0 1 2 4
1 3 0 2 0
3 0 0 0 1
4 0 1 1 5
Each row represent production order.
I want to create a graph with x-axis=PlantID's and y-axis are A,B,C,D with different bars.
In this way I can see that which factory has the most D error, which has A in one graph
I usually use plotly and seaborn but I couldn't find any solution for that, y-axis is single column in every example
Thanks in advance,
Seaborn likes its data in long or wide-form.
As mentioned above, seaborn will be most powerful when your datasets have a particular organization. This format ia alternately called “long-form” or “tidy” data and is described in detail by Hadley Wickham in this academic paper. The rules can be simply stated:
Each variable is a column
Each observation is a row
The following code converts the original dataframe to a long form dataframe.
By stacking the columns on top of each other such that every row corresponds to a single record that specifies the column name and the value (the count).
import numpy as np
import pandas as pd
import seaborn as sns
# Generating some data
N = 20
PlantID = np.random.choice(np.arange(1, 4), size=N, replace=True)
data = dict((k, np.random.randint(0, 50, size=N)) for k in ['A', 'B', 'C', 'D'])
df = pd.DataFrame(data, index=PlantID)
df.index = df.index.set_names('PlantID')
# Stacking the columns and resetting the index to create a longformat. (And some renaming)
df = df.stack().reset_index().rename({'level_1' : 'column', 0: 'count'},axis=1)
sns.barplot(x='PlantID', y='count', hue='column', data=df)
Pandas has really clever built-in plotting functionality:
df.plot(kind='bar')
plt.show()
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()
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...
I would like to plot a heatmap from a DataFrame in pandas. The data looks like
df = pd.DataFrame({"A": np.random.random(100), "B": np.random.random(100), "C": np.random.random(100)})
To show how C changes as a function of A and B, I want to bin the data based on A and B and calculate the average value of C in each bin, finally the heatmap has A and B as X-axis and Y-axis, and the color indicates the corresponding C value.
I tried to use Seaborn.heatmap, but the function accepts square dataset, which means I should bin the data first.
Is there a way to directly generate what I want from the DataFrame?
If not, how can I bin DataFrame into 2-D grids?
I know pandas.cut can do the trick, but it seems only be able to cut based on one column one time. Of corse I can write tedious function to pipeline 'two cuts', but I am wondering if there is some simple way to do the task.
Scatter plot can give similar results but I want heatmap, something like this, not this.
Something like this?
>>> df.groupby([pd.cut(df.A, 3), pd.cut(df.B, 3)]).C.mean().unstack()
B (0.00223, 0.335] (0.335, 0.666] (0.666, 0.998]
A
(0.000763, 0.334] 0.579832 0.454004 0.349740
(0.334, 0.667] 0.587145 0.677880 0.559560
(0.667, 1] 0.566409 0.496061 0.420541