Aim: plot a column chart representing concentration values at discrete sites
Problem: the 14 site labels are numeric, so I think ggplot2 is assuming continuous data and adding spaces for what it sees as 'missing numbers'. I only want 14 columns with 14 marks/labels, relative to the 14 values in the dataframe. I've tried assigning the sites as factors and characters but neither work.
Also, how do you ensure the y-axis ends at '0', so the bottom of the columns meet the x-axis?
Thanks
Data:
Sites: 2,4,6,7,8,9,10,11,12,13,14,15,16,17
Concentration: 10,16,3,15,17,10,11,19,14,12,14,13,18,16
You have two questions in one with two pretty straightforward answers:
1. How to force a discrete axis when your column is a continuous one? To make ggplot2 draw a discrete axis, the data must be discrete. You can force your numeric data to be discrete by converting to a factor. So, instead of x=Sites in your plot code, use x=as.factor(Sites).
2. How to eliminate the white space below the columns in a column plot? You can control the limits of the y axis via the scale_y_continuous() function. By default, the limits extend a bit past the actual data (in this case, from 0 to the max Concentration). You can override that behavior via the expand= argument. Check the documentation for expansion() for more details, but here I'm going to use mult=, which uses a multiplication to find the new limits based on the data. I'm using 0 for the lower limit to make the lower axis limit equal the minimum in your data (0), and 0.05 as the upper limit to expand the chart limits about 5% past the max value (this is default, I believe).
Here's the code and resulting plot.
library(ggplot2)
df <- data.frame(
Sites = c(2,4,6,7,8,9,10,11,12,13,14,15,16,17),
Concentration = c(10,16,3,15,17,10,11,19,14,12,14,13,18,16)
)
ggplot(df, aes(x=as.factor(Sites), y=Concentration)) +
geom_col(color="black", fill="lightblue") +
scale_y_continuous(expand=expansion(mult=c(0, 0.05))) +
theme_bw()
Originally I had a dataframe containing power consumption of some devices like this:
and I wanted to plot power consumption vs time for different devices, one plot per one of 6 possible dates. After grouping by date I got plots like this one (for each group = date):
Then I tried to create similar plot, but switch date and device roles so that it is grouped by device and colored by date. In order to do it I prepared this dataframe:
It is similar to the previous one, but has many NaN values due to differing measurement times. I thought it won't be a problem, but then after grouping by device, subplots look like this one (ex is just a name of sub-dataframe extracted from loop going through groups = devices):
This is the ex dataframe (mean lag between observations is around 20 seconds)
Question: What should I do to make plot grouped by device look like ones grouped by date? (I'd like to use ex dataframe but handle NaNs somehow.)
I found solution in answer to similar question: ex.interpolate(method='linear').plot(). This line will fill gaps between data points via interpolation between plotting. This is the result:
Another thing that can help is adding .plot(marker='o', ms = 3) which won't fill gaps between points, but at least will make points visible (previously some points, mainly the peaks in energy consumption were too small in scale of whole plot). This is the result:
I want to plot 5 different data frames in 1 plot. Containing the same measurement but done at different times. The plot should share the x-Axis for all measurement.
First thing i did was to calculate the time between the measurement points. It differs between 5-10 ms but sometimes also big gaps of 200 ms.
Then i calculated the running sum over this column. Then i set this column as the index (dtype "timedelta64[ns]")
Now i want to plot those 5 times.series in one plot which share the x-Axis (as time in ms)
But i don´t now how because they have almost no common index together. The plot should have one common x-Axis from 0-3 seconds containing the 5 measurements.
Thank you!
2 Example DataFrames:
example for measuremt01
example for measuremt02
I am having a hard time of plotting the density of Pandas time series.
I have a data frame with perfectly organised timestamps, like below:
It's a web log, and I want to show the density of the timestamp, which indicates how many visitors in certain period of time.
My solution atm is extracting the year, month, week and day of each timestamp, and group them. Like below:
But I don't think it would be a efficient way of dealing with time. And I couldn't find any good info on this, more of them are about plot the calculated values on a date or something.
So, anybody have any suggestions on how to plot Pandas time series?
Much appreciated!
The best way to compute the values you want to plot is to use Series.resample; for example, to aggregate the count of dates daily, use this:
ser = pd.Series(1, index=dates)
ser.resample('D').sum()
The documentation there has more details depending on exactly how you want to resample & aggregate the data.
If you want to plot the result, you can use Pandas built-in plotting capabilities; for example:
ser.resample('D').sum().plot()
More info on plotting is here.
I want to compute means with bootstrap confidence intervals for some subsets of a dataframe; the ultimate goal is to produce bar graphs of the means with bootstrap confidence intervals as the error bars. My data frame looks like this:
ATG12 Norm ATG5 Norm ATG7 Norm Cancer Stage
5.55 4.99 8.99 IIA
4.87 5.77 8.88 IIA
5.98 7.88 8.34 IIC
The subsets I'm interested in are every combination of Norm columns and cancer stage. I've managed to produce a table of means using:
df.groupby('Cancer Stage')['ATG12 Norm', 'ATG5 Norm', 'ATG7 Norm'].mean()
But I need to compute bootstrap confidence intervals to use as error bars for each of those means using the approach described here: http://www.randalolson.com/2012/08/06/statistical-analysis-made-easy-in-python/
It boils down to:
import scipy
import scikits.bootstraps as bootstraps
CI = bootstrap.ci(data=Series, statfunction=scipy.mean)
# CI[0] and CI[1] are your low and high confidence intervals
I tried to apply this method to each subset of data with a nested-loop script:
for i in data.groupby('Cancer Stage'):
for p in i.columns[1:3]: # PROBLEM!!
Series = i[p]
print p
print Series.mean()
ci = bootstrap.ci(data=Series, statfunction=scipy.mean)
Which produced an error message
AttributeError: 'tuple' object has no attribute called 'columns'
Not knowing what "tuples" are, I have some reading to do but I'm worried that my current approach of nested for loops will leave me with some kind of data structure I won't be able to easily plot from. I'm new to Pandas so I wouldn't be surprised to find there's a simpler, easier way to produce the data I'm trying to graph. Any and all help will be very much appreciated.
The way you iterate over the groupby-object is wrong! When you use groupby(), your data frame is sliced along the values in your groupby-column(s), together with these values as group names, forming a so-called "tuple":
(name, dataforgroup). The correct recipe for iterating over groupby-objects is
for name, group in data.groupby('Cancer Stage'):
print name
for p in group.columns[0:3]:
...
Please read more about the groupby-functionality of pandas here and go through the python-reference in order to understand what tuples are!
Grouping data frames and applying a function is essentially done in one statement, using the apply-functionality of pandas:
cols=data.columns[0:2]
for col in columns:
print data.groupby('Cancer Stage')[col].apply(lambda x:bootstrap.ci(data=x, statfunction=scipy.mean))
does everything you need in one line, and produces a (nicely plotable) series for you
EDIT:
I toyed around with a data frame object I created myself:
df = pd.DataFrame({'A':range(24), 'B':list('aabb') * 6, 'C':range(15,39)})
for col in ['A', 'C']:
print df.groupby('B')[col].apply(lambda x:bootstrap.ci(data=x.values))
yields two series that look like this:
B
a [6.58333333333, 14.3333333333]
b [8.5, 16.25]
B
a [21.5833333333, 29.3333333333]
b [23.4166666667, 31.25]