Merging dataframes in Pandas is taking a surprisingly long time
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I'm trying to merge a list of time series dataframes (could be over 100) using Pandas. The largest file has a size of $approx$ 50 MB. The number of rows and columns vary (for instance, one file could have 45,000 rows and 20 columns, another has 100 rows and 900 columns), but they all have common columns of "SubjectID" and "Date", which I'm using to merge the dataframes. I read the dataframes into the list from CSV files, where I know the datatypes for each column.
However, when I try to merge even 10 of the dataframes, it takes around 7 hours, and for all 100, my kernel crashes. Reading all the files in takes about a minute. These aren't files that I would consider "big data" or even large files, like in the posts here, here, or here, so I'm surprised it's taking so long to merge them all. For all 100 dataframes, I'd expect the final dataframe size to be about 1.5 GB, but even that I think Pandas could handle. My code to merge the dataframes is
merged_df = reduce(lambda l,r: l.merge(r, on=['SubjectID', 'Date'], how='outer', suffixes=['_COPYL', '_COPYR']), df_list)
because I want columns to be added on at if they don't already exist for a subjectID and date (I take care of duplicates later).
So far, I've tried reducing the memory of each column datatype by downcasting, different ways to merge in Pandas and CSV files, converting to a sparse dataframe (my data does have a fair amount of NaN), using Dask, and I'm at the point where I'm considering creating a relational database using sqllite in Python. My versions are python-64 3.7.0, pandas 0.23.4, Ipython 6.5.0, using a Mac OSX Darwin with 32 GB memory. I would think my computer should have no trouble doing this, but it does. Is there really no better way or am I doing something wrong that I'm missing?
python pandas bigdata data-cleaning
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I'm trying to merge a list of time series dataframes (could be over 100) using Pandas. The largest file has a size of $approx$ 50 MB. The number of rows and columns vary (for instance, one file could have 45,000 rows and 20 columns, another has 100 rows and 900 columns), but they all have common columns of "SubjectID" and "Date", which I'm using to merge the dataframes. I read the dataframes into the list from CSV files, where I know the datatypes for each column.
However, when I try to merge even 10 of the dataframes, it takes around 7 hours, and for all 100, my kernel crashes. Reading all the files in takes about a minute. These aren't files that I would consider "big data" or even large files, like in the posts here, here, or here, so I'm surprised it's taking so long to merge them all. For all 100 dataframes, I'd expect the final dataframe size to be about 1.5 GB, but even that I think Pandas could handle. My code to merge the dataframes is
merged_df = reduce(lambda l,r: l.merge(r, on=['SubjectID', 'Date'], how='outer', suffixes=['_COPYL', '_COPYR']), df_list)
because I want columns to be added on at if they don't already exist for a subjectID and date (I take care of duplicates later).
So far, I've tried reducing the memory of each column datatype by downcasting, different ways to merge in Pandas and CSV files, converting to a sparse dataframe (my data does have a fair amount of NaN), using Dask, and I'm at the point where I'm considering creating a relational database using sqllite in Python. My versions are python-64 3.7.0, pandas 0.23.4, Ipython 6.5.0, using a Mac OSX Darwin with 32 GB memory. I would think my computer should have no trouble doing this, but it does. Is there really no better way or am I doing something wrong that I'm missing?
python pandas bigdata data-cleaning
asked 15 mins ago
m42op64m42op64
11
New contributor
m42op64 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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add a comment |
$begingroup$
I'm trying to merge a list of time series dataframes (could be over 100) using Pandas. The largest file has a size of $approx$ 50 MB. The number of rows and columns vary (for instance, one file could have 45,000 rows and 20 columns, another has 100 rows and 900 columns), but they all have common columns of "SubjectID" and "Date", which I'm using to merge the dataframes. I read the dataframes into the list from CSV files, where I know the datatypes for each column.
However, when I try to merge even 10 of the dataframes, it takes around 7 hours, and for all 100, my kernel crashes. Reading all the files in takes about a minute. These aren't files that I would consider "big data" or even large files, like in the posts here, here, or here, so I'm surprised it's taking so long to merge them all. For all 100 dataframes, I'd expect the final dataframe size to be about 1.5 GB, but even that I think Pandas could handle. My code to merge the dataframes is
merged_df = reduce(lambda l,r: l.merge(r, on=['SubjectID', 'Date'], how='outer', suffixes=['_COPYL', '_COPYR']), df_list)
because I want columns to be added on at if they don't already exist for a subjectID and date (I take care of duplicates later).
So far, I've tried reducing the memory of each column datatype by downcasting, different ways to merge in Pandas and CSV files, converting to a sparse dataframe (my data does have a fair amount of NaN), using Dask, and I'm at the point where I'm considering creating a relational database using sqllite in Python. My versions are python-64 3.7.0, pandas 0.23.4, Ipython 6.5.0, using a Mac OSX Darwin with 32 GB memory. I would think my computer should have no trouble doing this, but it does. Is there really no better way or am I doing something wrong that I'm missing?
python pandas bigdata data-cleaning
asked 15 mins ago
m42op64m42op64
11
New contributor
m42op64 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm trying to merge a list of time series dataframes (could be over 100) using Pandas. The largest file has a size of $approx$ 50 MB. The number of rows and columns vary (for instance, one file could have 45,000 rows and 20 columns, another has 100 rows and 900 columns), but they all have common columns of "SubjectID" and "Date", which I'm using to merge the dataframes. I read the dataframes into the list from CSV files, where I know the datatypes for each column.
However, when I try to merge even 10 of the dataframes, it takes around 7 hours, and for all 100, my kernel crashes. Reading all the files in takes about a minute. These aren't files that I would consider "big data" or even large files, like in the posts here, here, or here, so I'm surprised it's taking so long to merge them all. For all 100 dataframes, I'd expect the final dataframe size to be about 1.5 GB, but even that I think Pandas could handle. My code to merge the dataframes is
merged_df = reduce(lambda l,r: l.merge(r, on=['SubjectID', 'Date'], how='outer', suffixes=['_COPYL', '_COPYR']), df_list)
because I want columns to be added on at if they don't already exist for a subjectID and date (I take care of duplicates later).
So far, I've tried reducing the memory of each column datatype by downcasting, different ways to merge in Pandas and CSV files, converting to a sparse dataframe (my data does have a fair amount of NaN), using Dask, and I'm at the point where I'm considering creating a relational database using sqllite in Python. My versions are python-64 3.7.0, pandas 0.23.4, Ipython 6.5.0, using a Mac OSX Darwin with 32 GB memory. I would think my computer should have no trouble doing this, but it does. Is there really no better way or am I doing something wrong that I'm missing?
python pandas bigdata data-cleaning
python pandas bigdata data-cleaning
asked 15 mins ago
m42op64m42op64
11
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m42op64 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 15 mins ago
m42op64m42op64
11
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asked 15 mins ago
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asked 15 mins ago
m42op64m42op64
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asked 15 mins ago
m42op64m42op64
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11
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m42op64 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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m42op64 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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