Is there a the difference between the Pandas plot.density() and plot.kde() function?
According to the Pandas API Reference, there is no difference between plot.density() and plot.kde(), other than their name. These 2 functions do exactly the same thing.
As mentioned by #RichieK in the comments, both API Reference pages take you to the same source code when you click on [source] in the top right corner of the page. Thus confirming the functions are exactly the same.
Just wanted to add, in the source code you can find :
density = kde
See here: https://github.com/pandas-dev/pandas/blob/e8093ba372f9adfe79439d90fe74b0b5b6dea9d6/pandas/plotting/_core.py#L1456
Related
I am an elementary Python programmer and have been using this module called "Pybaseball" to analyze sabermetrics data. When using this module, I came across a problem when trying to retrieve information from the program. The program reads a CSV file from any baseball stats site and outputs it onto a program for ease of use but the problem is that some of the information is not shown and is instead all replaced with a "...". An example of this is shown:
from pybaseball import batting_stats_range
data = batting_stats_range('2017-05-01', '2017-05-08')
print(data.head())
I should be getting:
https://github.com/jldbc/pybaseball#batting-stats-hitting-stats-for-players-within-seasons-or-during-a-specified-time-period
But the information is cutoff from 'TM' all the way to 'CS' and is replaced with a ... on my code. Can someone explain to me why this happens and how I can prevent it?
As the docs states, head() is meant for "quickly testing if your object has the right type of data in it." So, it is expected that some data may not show because it is collapsed.
If you need to analyze the data with more detail you can access specific columns with other methods.
For example, using iloc(). You can read more about it here, but essentially you can "ask" for a slice of those columns and then apply a new slice to get only nrows.
Another example would be loc(), docs here. The main difference being that loc() uses labels (column names) to filter data instead of numerical order of columns. You can filter a subset of specific columns and then get a sample of rows from that.
So, to answer your question "..." is pandas's way of collapsing data in order to get a prettier view of the results.
I am trying to replicate a paper whose code was written in Stata for my course project using Python. I have difficulty replicating the results from a collapse command in their do-file. The corresponding line in the do-file is
collapse lexptot, by(clwpop right)
while I have
df.groupby(['cwpop', 'right'])['lexptot'].agg(['mean'])
The lexptot variable is the logarithm of a variable 'exptot' which I calculated previously using np.log(dfs['exptot]).
Does anyone have an idea what is going wrong here? The means I calculate are typically around 1.5 higher than the means calculated in Stata.
Once you update the question with more relevant details maybe I can answer more. But this is what I think might help you!
df.groupby(['cwpop', 'right']).mean()['lexptot']
I have a function that creates new columns and data of a pandas dataframe. I am now trying to move these testing method to dask to be able to test on larger sets of data. I am having issue finding the problem as my function does not throw any errors,just that the data is wrong. I came to the conclusion that it must be a an issue with the fuctions I am calling. What am I missing? I think its here but if it was, I would think that python would give me an error but its not. I recently saw that transform is not supported. I also believe between_time is not supported.
validSignalTime=(df1.index.time>=en)&(df1.index.time<=NoSignalAfter)
time_condition=df1.index.isin(df1.between_time(st, en, include_start=True,
include_end=False).index)
df1['Entry_Price']=df1[time_condition].groupby(df1[time_condition].index.date)['High'].transform('cummax')
I understand that to some this might look vague but: what part of documentation should i look at when trying to do the following:
I have:
a) a table: a dataframe created using pandas to be precise
b) a chart coming from any number of chart-producing-libraries (plotlib / seaborn / etc)
and i want to simply want to prep them in a (less advanced) fashion such as the one displayed here (http://www.reportlab.com/media/imadj/data/RLIMG_41e9a00cdb0698a0dca740ed26ffa4dc.PDF)
I am not looking for an exact solution but it would be nice to be pointed in the right direction in terms of documentation.
I have been trying to get a proper documentation for the freq arguments associated with pandas. For example to resample a dataframe we can do something like
df.resample(rule='W', how='sum')
which will resample this weekly. I was wondering what are the other options and how can I define custom frequency/rules.
EDIT : To clarify I am looking at what are the other legal options for rule
http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
And, almost immediately below that: W-SAT and others.
I'll admit, links to this particular piece of documentation are pretty scarce. More general frequencies can be represented by supplying a DateOffset instance. Even more general resamplings can be done via groupby.