Simple question, and my google-fu is not strong enough to find the right term to get a solid answer from the documentation. Any term I look for that includes either change or modify leads me to questions like 'How to change column name....'
I am reading in a large dataframe, and I may be adding new columns to it. These columns are based on interpolation of values on a row by row basis, and the simple numbers of rows makes this process a couple hours in length. Hence, I save the dataframe, which also can take a bit of time - 30 seconds at least.
My current code will always save the dataframe, even if I have not added any new columns. Since I am still developing some plotting tools around it, I am wasting a lot of time waiting for the save to finish at the termination of the script needlessly.
Is there a DataFrame attribute I can test to see if the DataFrame has been modified? Essentially, if this is False I can avoid saving at the end of the script, but if it is True then a save is necessary. This simple one line if will save me a lot of time and a lost of SSD writes!
You can use:
df.equals(old_df)
You can read the it's functionality in pandas' documentation. It basically does what you want, returning True only if both DataFrames are equal, and it's probably the fastest way to do it since it's an implementation of pandas itself.
Notice you need to use .copy() when assigning old_df before changes in your current df, otherwise you might pass the dataframe by reference and not by value.
Related
I have the following dataframe where I want to assign the bottom 1% value to a new column. When I do this calculation with using the ".loc" notification, it takes around 10 seconds for using .loc assignment, where the alternative solution is only 2 seconds.
df_temp = pd.DataFrame(np.random.randn(100000000,1),columns=list('A'))
%time df_temp["q"] = df_temp["A"].quantile(0.01)
%time df_temp.loc[:, "q1_loc"] = df_temp["A"].quantile(0.01)
Why is the .loc solution slower? I understand using the .loc solution is safer, but if I want to assign data to all indices in the column, what can go wrong with the direct assignment?
.loc is searching along the entirety of indices and columns (in this case, only 1 column) in your df along the whole axes, which is time consuming and perhaps redundant, in addition to figuring out the quantiles of df_temp['A'] (which is negligible as far as calculation time). Your direct assignment method, on the other hand, is just parsing df_temp['A'].quantile(0.01), and assigning df_temp['q']. It doesn't need to exhaustively search the indices/columns of your df.
See this answer for a similar description of the .loc method.
As far as safety is concerned, you are not using chained indexing, so you're probably safe (you're not trying to set anything on a copy of your data, it's being set directly on the data itself). It's good to be aware of the potential issues with not using .loc (see this post for a nice overview of SettingWithCopy warnings), but I think that you're OK as far as that goes.
If you want to be more explicit about your column creation, you could do something along the lines of df = df.assign(q=df_temp["A"].quantile(0.01)). It won't really change performance (I don't think), nor the result, but it allows you to see that you're explicitly assigning a new column to your existing dataframe (and thus not setting anything on a copy of said dataframe).
I often need to construct a bi-temporal Pandas DataFrame, with [CURR_DATE, HIST_DATE] as MultiIndex, and a dozen columns of numerical, categorical and text data. Note for every CURR_DATE, the HIST_DATE will cycle through, say, 3 years, and the rest of the row largely depend on HIST_DATE describing information on the HIST_DATE, with very infrequent changes (due to information on that HIST_DATE get updated on certain CURR_DATE).
As you can see, this DataFrame has lots of repeated information. But it gets copied again and again, making the entire DataFrame very memory inefficient. (Comparatively, dict object would allow references hence pointing to the same underlying object to be highly efficient.)
Question: what would be a better way to construct the DataFrame to still allow bi-temporal processing ability (eg. DATA_DATE needs to be joined with some other DataFrame, and HIST_DATE needs to be joined with a 3rd DataFrame), while making the entire DataFrame a lot more memory efficient / having a much smaller memory footprint?
(Feel free to ask me to clarify if the question is not clear.)
I am using three dataframes to analyze sequential numeric data - basically numeric data captured in time. There are 8 columns, and 360k entries. I created three identical dataframes - one is the raw data, the second a "scratch pad" for analysis and a third dataframe contains the analyzed outcome. This runs really slowly. I'm wondering if there are ways to make this analysis run faster? Would it be faster if instead of three separate 8 column dataframes I had one large one 24 column dataframe?
Use cProfile and lineprof to figure out where the time is being spent.
To get help from others, post your real code and your real profile results.
Optimization is an empirical process. The little tips people have are often counterproductive.
Most probably it doesn't matter because pandas stores each column separately anyway (DataFrame is a collection of Series). But you might get better data locality (all data next to each other in memory) by using a single frame, so it's worth trying. Check this empirically.
Rereading this post I am realizing I could have been clearer. I have been using write statement like:
dm.iloc[p,XCol] = dh.iloc[x,XCol]
to transfer individual cells of one dataframe (dh) to a different row of a second dataframe (dm). It ran very slowly but I needed this specific file sorted and I just lived with the performance.
According to "Learning Pandas" by Michael Heydt, pg 146, ".iat" is faster than ".iloc" for extracting (or writing) scalar values from a dataframe. I tried it and it works. With my original 300k row files, run time was 13 hours(!) using ".iloc", same datafile using ".iat" ran in about 5 minutes.
Net - this is faster:
dm.iat[p,XCol] = dh.iat[x,XCol]
I need to do an apply on a dataframe using inputs from multiple rows. As a simple example, I can do the following if all the inputs are from a single row:
df['c'] = df[['a','b']].apply(lambda x: awesome stuff, axis=1)
# or
df['d'] = df[['b','c']].shift(1).apply(...) # to get the values from the previous row
However, if I need 'a' from the current row, and 'b' from the previous row, is there a way to do that with apply? I could add a new 'bshift' column and then just use df[['a','bshift']] but it seems there must be a more direct way.
Related but separate, when accessing a specific value in the df, is there a way to combine labeled indexing with integer-offset? E.g. I know the label of the current row but need the row before. Something like df.at['labelIknow'-1, 'a'] (which of course doesn't work). This is for when I'm forced to iterate through rows. Thanks in advance.
Edit: Some info on what I'm doing etc. I have a pandas store containing tables of OHLC bars (one table per security). When doing backtesting, currently I pull the full date range I need for a security into memory, and then resample it into a frequency that makes sense for the test at hand. Then I do some vectorized operations for things like trade entry signals etc. Finally I loop over the data from start to finish doing the actual backtest, e.g. checking for trade entry exit, drawdown etc - this looping part is the part I'm trying to speed up.
This should directly answer your question and let you use apply, although I'm not sure it's ultimately any better than a two-line solution. It does avoid creating extra variables at least.
df['c'] = pd.concat([ df['a'], df['a'].shift() ], axis=1).apply(np.mean,axis=1)
That will put the mean of 'a' values from the current and previous rows into 'c', for example.
This isn't as general, but for simpler cases you can do something like this (continuing the mean example):
df['c'] = ( df['a'] + df['a'].shift() ) / 2
That is about 10x faster than the concat() method on my tiny example dataset. I imagine that's as fast as you could do it, if you can code it in that style.
You could also look into reshaping the data with stack() and hierarchical indexing. That would be a way to get all your variables into the same row but I think it will likely be more complicated than the concat method or just creating intermediate variables via shift().
For the first part, I don't think such a thing is possible. If you update on what you actually want to achieve, I can update this answer.
Also looking at the second part, your data structure seems to be relying an awfully lot on the order of rows. This is typically not how you want to manage your databases. Again, if you tell us what your overall goal is, we may hint you towards a solution (and potentially a better way to structure the data base).
Anyhow, one way to get the row before, if you know a given index label, is to do:
df.ix[:'labelYouKnow'].iloc[-2]
Note that this is not the optimal thing to do efficiency-wise, so you may want to improve your your db structure in order to prevent the need for doing such things.
I have a live feed of logging data coming in through the network. I need to calculate live statistics, like the one in my previous question. How would I design this module? I mean, it seems unrealistic (read, bad design) to keep applying a groupby function to the entire df every single time a message arrives. Can I just update one row and its calculated column gets auto-updated?
JFYI, I'd be running another thread that will print read values from the df and print to the a webpage every 5 seconds or so..
Of course, I could run groupby-apply every 5 seconds instead of doing it in real time, but I thought it'd be better to keep the df and the calculation independent of the printing module.
Thoughts?
groupby is pretty damn fast, and if you preallocate slots for new items you can make it even faster. In other words, try it and measure it for a reasonable amount of fake data. If it's fast enough, use pandas and move on. You can always rewrite it later.