Solution, if need create one big DataFrame if need processes all data at once (what is possible, but not recommended):
Then use concat for all chunks to df, because type of output of function:
df = pd.read_csv('Check1_900.csv', sep='\t', iterator=True, chunksize=1000)
isn't dataframe, but pandas.io.parsers.TextFileReader - source.
tp = pd.read_csv('Check1_900.csv', sep='\t', iterator=True, chunksize=1000)
print tp
#<pandas.io.parsers.TextFileReader object at 0x00000000150E0048>
df = pd.concat(tp, ignore_index=True)
I think is necessary add parameter ignore index to function concat, because avoiding duplicity of indexes.
EDIT:
But if want working with large data like aggregating, much better is use dask, because it provides advanced parallelism.
Solution, if need create one big DataFrame if need processes all data at once (what is possible, but not recommended):
Then use concat for all chunks to df, because type of output of function:
df = pd.read_csv('Check1_900.csv', sep='\t', iterator=True, chunksize=1000)
isn't dataframe, but pandas.io.parsers.TextFileReader - source.
tp = pd.read_csv('Check1_900.csv', sep='\t', iterator=True, chunksize=1000)
print tp
#<pandas.io.parsers.TextFileReader object at 0x00000000150E0048>
df = pd.concat(tp, ignore_index=True)
I think is necessary add parameter ignore index to function concat, because avoiding duplicity of indexes.
EDIT:
But if want working with large data like aggregating, much better is use dask, because it provides advanced parallelism.
You do not need concat here. It's exactly like writing sum(map(list, grouper(tup, 1000))) instead of list(tup). The only thing iterator and chunksize=1000 does is to give you a reader object that iterates 1000-row DataFrames instead of reading the whole thing. If you want the whole thing at once, just don't use those parameters.
But if reading the whole file into memory at once is too expensive (e.g., takes so much memory that you get a MemoryError, or slow your system to a crawl by throwing it into swap hell), that's exactly what chunksize is for.
The problem is that you named the resulting iterator df, and then tried to use it as a DataFrame. It's not a DataFrame; it's an iterator that gives you 1000-row DataFrames one by one.
When you say this:
My problem is I don't know how to use stuff like these below for the whole df and not for just one chunk
The answer is that you can't. If you can't load the whole thing into one giant DataFrame, you can't use one giant DataFrame. You have to rewrite your code around chunks.
Instead of this:
df = pd.read_csv('Check1_900.csv', sep='\t', iterator=True, chunksize=1000)
print df.dtypes
customer_group3 = df.groupby('UserID')
… you have to do things like this:
for df in pd.read_csv('Check1_900.csv', sep='\t', iterator=True, chunksize=1000):
print df.dtypes
customer_group3 = df.groupby('UserID')
Often, what you need to do is aggregate some data—reduce each chunk down to something much smaller with only the parts you need. For example, if you want to sum the entire file by groups, you can groupby each chunk, then sum the chunk by groups, and store a series/array/list/dict of running totals for each group.
Of course it's slightly more complicated than just summing a giant series all at once, but there's no way around that. (Except to buy more RAM and/or switch to 64 bits.) That's how iterator and chunksize solve the problem: by allowing you to make this tradeoff when you need to.
How to set custom chunksize parameters to read csv into pandas data frame?
python - Refactoring pandas using an iterator via chunksize - Bioinformatics Stack Exchange
Does pandas.read_csv's chunks have context on the entire dataset?
python - Pandas Chunksize iterator - Stack Overflow
Hello, so I have a massive 5GB+ csv file I am trying to read into a pandas data frame in python. The csv file has over 100 million rows of data. The data is a simple timeseries data set, and so a single timestamp column and then a corresponding value column, where each row represents a single second, proceeding in chronological order. Though when trying to read this in as a pandas data frame, given the enormous size of the csv file, I run out of memory to allocate to reading in this data on my machine. To avoid this problem, I am trying to read in this csv data in chunks, using the following code:
Chunksize = 2500000
for chunk in pd.read_csv("my_file.csv", chunksize=Chunksize):
print(chunk.head()) This works, where I am able to read in my csv file into data frame chunks of 2,500,000 rows each (the last chunk would of course be the remainder of < 2,500,000 rows).
However, I want an explicit reason for my chunk size, as opposed to just a "best judgement" selection, such as the 2,500,000 row chunk size I use above. What I want to figure out is, how can I set my chunk size to be custom based on a given parameter? Specifically, I want each of my chunks to be all of the rows corresponding to unique months in my time series data set. And so let's say this time series dataset has for example 3 years, 5 months, and 9 days of data, and so 3x12 = 36 months + 5 months = 41 months and 9 days of data = 42 chunks, where I have 41 chunks of full month-long second-resolution data and then the last chunk made up of 9 days worth of 1-second resolution data.
How can I augment the chunksize argument in pd.read_csv() to accommodate a custom parameter such as delimiting by months? I am guessing this would involve some sort of manipulation in the timestamp as a datetime object, but I am not sure how to actually specify this delineation, since the chunksize argument just requires a single value. I would appreciate any guidance on this matter, thank you!
Don't have a sample dataset to play with right now so thought I'd just ask.
Is chunksize basically just a more automated way of setting nrows and going through all the rows in batches, or is there an advantage to chunking where you could do operations that require context on the whole dataset?
For example, for a dataset of transactions, say I need to pull all the biggest orders for each account, and the accounts are not clumped up within the dataset. So when I iterate over the partitions, taking MAX of the order-size column would not be accurate.
Assuming the dataset is too big to fit in memory at once, I can't figure out how I'd go about this (besides using completely different tools/packages, which is out of scope for now).
You have some problems with your logic, we want to loop over each chunk in the data, not the data itself.
The 'chunksize' argument gives us a 'textreader object' that we can iterate over.
import pandas as pd
data=pd.read_table('datafile.txt',sep='\t',chunksize=1000)
for chunk in data:
chunk = chunk[chunk['visits']>10]
chunk.to_csv('data.csv', index = False, header = False)
You will need to think about how to handle your header!
When you pass a chunksize or iterator=True, pd.read_table returns a TextFileReader that you can iterate over or call get_chunk on. So you need to iterate or call get_chunk on data.
So proper handling of your entire file might look something like
import pandas as pd
data = pd.read_table('datafile.txt',sep='\t',chunksize=1000, iterator=True)
with open('data.csv', 'a') as f:
for chunk in data:
chunk[chunk.visits > 10].to_csv(f, sep=',', index=False, header=False)