I have a dataframe that contains a column value of 'A','B','C','D'... This is just a grouping of some sorts. I wanted to produce a histogram with the column values vs its count.
import seaborn as sns
sns.distplot(dfGroupingWithoutNan['patient_group'])
This produced an error:
TypeError: unsupported operand type(s) for /: 'str' and 'int'
I thought maybe because im not familiar with distplot, i'm not using it the right way. I was thinking, i can just pass a Series into it and it will be able to determine the counts for each values and display them in the histogram accordingly.
Anyway, i thought of other solution and this is what I came up with.
series1 = dfGroupingWithoutNan['patient_group'].value_counts()
dfPatientGroup = pd.DataFrame( {'levels' : series1.index, 'level_values' : series1.values})
sns.set_style("whitegrid")
sns.barplot(x="levels", y="level_values", data=dfPatientGroup)
This time I was able to produce a plot of each values versus its count though using a bar plot.
I just wanted to ask, was there any other way to do this, like how it would have worked if i use the distplot? Also, do i really need to create a new dataframe just to have some sort of repository that holds the values and the count? I was thinking, wont it be possible for the distplot to determine the counts automatically without going through the hassle of creating a new dataframe?
I would use a Counter to do this. The logic is very similar to what you are doing, but you don't need to create an extra dataframe:
from collections import Counter
cnt = Counter(dfGroupingWithoutNan.patient_group)
sns.barplot(x=cnt.keys(), y=cnt.values())
I'm not aware of any solution that automatically handle string values in seaborn or matplotlib histograms.
Related
I created a graph using DOGE crypto data:
import pandas as pd
import csv
df2 = pd.read_csv("https://raw.githubusercontent.com/peoplecure/pandoras-box/master/doge.csv")
plt.plot(df2['begins_at'], df2['open_price'])
plt.show()
Above graph looks fine. But, when I try to create a graph using another method with the exact same data, the graph looks totally off
from pandas import DataFrame
df = DataFrame (DOGE_data)
plt.plot(df['begins_at'], df['open_price'])
plt.show()
Regrettably, I don't have a way to share the data in the second method. However, data used in the first graph was created by df. I was hoping if anyone has any idea what may be going on here.
The messed up y-axis could be the hint: Usually, with numerical data, there would be 4-12 y-axis label ticks and markers. Then, usually, with non-numerical data, there is one tick for each "category".
Check the data type of y-data in the second dataset: df['open_price'].dtype
Here is a sample of the data I'm working with WellAnalyticalData I'd like to loop through each well name and create a time series chart for each parameter with sample date on the x-axis and the value on the y-axis. I don't think I want subplots, I'm just looking for individual plots of each analyte for each well. I've used pandas to try grouping by well name and then attempting to plot, but that doesn't seem to be the way to go. I'm fairly new to python and I think I'm also having trouble figuring out how to construct the loop statement. I'm running python 3.x and am using the matplotlib library to generate the plots.
so if I understand your question correctly you want one plot for each combination of Well and Parameter. No subplots, just a new plot for each combination. Each plot should have SampleDate on the x-axis and Value on the y-axis. I've written a loop here that does just that, although you'll see that since in your data has just one date per well per parameter, the plots are just a single dot.
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.DataFrame({'WellName':['A','A','A','A','B','B','C','C','C'],
'SampleDate':['2018-02-15','2018-03-31','2018-06-07','2018-11-14','2018-02-15','2018-11-14','2018-02-15','2018-03-31','2018-11-14'],
'Parameter':['Arsenic','Lead','Iron','Magnesium','Arsenic','Iron','Arsenic','Lead','Magnesium'],
'Value':[0.2,1.6,0.05,3,0.3,0.79,0.3,2.7,2.8]
})
for well in df.WellName.unique():
temp1 = df[df.WellName==well]
for param in temp1.Parameter.unique():
fig = plt.figure()
temp2 = temp1[temp1.Parameter==param]
plt.scatter(temp2.SampleDate,temp2.Value)
plt.title('Well {} and Parameter {}'.format(well,param))
a bit of a Python newb here. As a beginner it's easy to learn different functions and methods from training classes but it's another thing to learn how to "best" code in Python.
I have a simple scenario where I'm looking to plot a portion of a dataframe spdf. I only want to plot instances where speed is greater than 0 and use datetime as my X-axis. The way I've managed to get the job done seems awfully redundant to me:
ts = pd.Series(spdf[spdf['speed']>0]['speed'].values, index=spdf[spdf['speed']>0]['datetime'])
ts.dropna().plot(title='SP1 over Time')
Is there a better way to plot this data without specifying the subset of my dataframe twice?
You don't need to build a new Series. You can plot using your original df
df[df['col'] > 0]].plot()
In your case:
spdf[spdf['speed'] > 0].dropna().plot(title='SP1 over Time')
I'm not sure what your spdf object is or how it was created. If you'll often need to plot using the 'datetime' column you can set that to be the index of the df.If you're reading the data from a csv you can do this using the parse_dates keyword argument or it you already have the dfyou can change the index using df.set_index('datetime'). You can use df.info() to see what is currently being used at your index and its datatype.
I have the following code:
fig, ax = plt.subplots(1, 1)
calls["2016-12-24"].resample("1h").sum().plot(ax=ax)
calls["2016-12-25"].resample("1h").sum().plot(ax=ax)
calls["2016-12-26"].resample("1h").sum().plot(ax=ax)
which generates the following image:
How can I make this so the lines share the x-axis? In other words, how do I make them not switch days?
If you don't care about using the correct datetime as index, you could just reset the index as you suggested for all the series. This is going to overlap all the time series, if this is what you're trying to achieve.
# the below should
calls["2016-12-24"].resample("1h").sum().reset_index("2016-12-24").plot(ax=ax)
calls["2016-12-25"].resample("1h").sum().reset_index("2016-12-25").plot(ax=ax)
calls["2016-12-26"].resample("1h").sum().reset_index("2016-12-26").plot(ax=ax)
Otherwise you should try as well to resample the three columns at the same time. Have a go with the below but not knowing how your original dataframe look like, I'm not sure this will fit your case. You should post some more information about the input dataframe.
# have a try with the below
calls[["2016-12-24","2016-12-25","2016-12-26"].resample('1h').sum().plot()
I am new to pandas and matplotlib, but not to Python. I have two questions; a primary and a secondary one.
Primary:
I have a pandas boxplot with FICO score on the x-axis and interest rate on the y-axis.
My x-axis is all messed up since the FICO scores are overwriting each other.
I'd like to show only every 4th or 5th ticklabel on the x-axis for a couple of reasons:
in general it's less chart-junky
in this case it will allow the labels to actually be read.
My code snippet is as follows:
plt.figure()
loansmin = pd.read_csv('../datasets/loanf.csv')
p = loansmin.boxplot('Interest.Rate','FICO.Score')
I saved the return value in p as I thought I might need to manipulate the plot further which I do now.
Secondary:
How do I access the plot, subplot, axes objects from pandas boxplot.
p above is an matplotlib.axes.AxesSubplot object.
help(matplotlib.axes.AxesSubplot) gives a message saying:
'AttributeError: 'module' object has no attribute 'AxesSubplot'
dir(matplotlib.axes) lists Axes, Subplot and Subplotbase as in that namespace but no AxesSubplot. How do I understand this returned object better?
As I explored further I found that one could explore the returned object p via dir().
Doing this I found a long list of useful methods, amongst which was set_xticklabels.
Doing help(p.set_xticklabels) gave some cryptic, but still useful, help - essentially suggesting passing in a list of strings for ticklabels.
I then tried doing the following - adding set_xticklabels to the end of the last line in the above code effectively chaining the invocations.
plt.figure()
loansmin = pd.read_csv('../datasets/loanf.csv')
p=loansmin.boxplot('Interest.Rate','FICO.Score').set_xticklabels(['650','','','','','700'])
This gave the desired result. I suspect there's a better way as in the way matplotlib does it which allows you to show every n'th label. But for immediate use this works, and also allows setting labels where they are not periodic for whatever reason, if you need that.
As usual, writing out the question explicitly helped me find the answer. And if anyone can help me get to the underlying matplotlib object that is still an open question.
AxesSubplot (I think) is just another way to get at the Axes in matplotlib. set_xticklabels() is part of the matplotlib object oriented interface (on axes). So, if you were using something like pylab, you might use xticks(ticks, labels), but instead here you have to separate it into different calls ax.set_xticks(ticks), ax.set_xticklabels(labels). (where ax is an Axes object).
Let's say you only want to set ticks at 650 and 700. You could do the following:
ticks = labels = [650, 700]
plt.figure()
loansmin = pd.read_csv('../datasets/loanf.csv')
p=loansmin.boxplot('Interest.Rate','FICO.Score')
p.set_xticks(ticks)
p.set_xticklabels(labels)
Similarly, you can use set_xlim and set_ylim to do the equivalent of xlim() and ylim() in plt.