在不等行的情况下，如何通过公共列合并两个csv文件？

Question

I have a set of 100 files. 50 files containing census information for each US state. The other fifty are geographic data that need to be merged with the correct file for each state.

For each state, the census file and its corresponding geo file are related by a common variable, LOGRECNO, that is the 10th column in the census file and the 7th column in the geo file.

The problem is that the geo file has more rows than the census file; my census data does not cover certain subsets of geographic locations and hence has fewer rows than the geo data file.

How can I merge the census data with the geographic date (keeping only the rows/geo locations where census data exists, don't care about the rest)?

I am a newbie to Python and I somewhat know how to do basic csv file i/o in python. Manipulating 2 csvs at the same time is proving confusing.

Example:

sample_state_census.csv

Varname 1 Varname 2 ... Varname 10 (LOGRECNO) ... Varname 16000
xxx       xxx    ...       1             ...               xxx
xxx       xxx    ...       2             ...               xxx
...
...
xxx       xxx   ...        514           ...                xxx
xxx       xxx   ...        1312          ...                xxx
...
...
xxx       xxx   ...        1500          ...                xxx

sample_state_geo.csv

GeoVarname 1 GeoVarname 2 ... GeoVarname 7 (LOGRECNO) ... GeoVarname 65
yyy       yyy    ...       1             ...               yyy
yyy       yyy    ...       2             ...               yyy
...
...
yyy      yyy  ...        514           ...                yyy
yyy      yyy   ...        515          ...                yyy
...
...
yyy     yyy  ...        1500          ...                yyy

Expected output (don't merge rows for values of LOGRECNO that don't exist in sample_state_census.csv)

Varname 1 Varname 2 ... Varname 10 (LOGRECNO) GeoVarname 1 GeoVarname 2 ... GeoVarname 65 Varname 11... Varname 16000 
xxx       xxx    ...       1  yyy yyy ... yyy xxx            ...               xxx
xxx       xxx    ...       2 yyy yyy ... yyy xxx            ...               xxx
...
...
xxx       xxx   ...        514    yyy yyy ... yyy xxx       ...                xxx
xxx       xxx   ...        1312      yyy yyy ... yyy xxx    ...                xxx
...
...
xxx       xxx   ...        1500    yyy yyy ... yyy xxx      ...                xxx

Answer 1

Read data from the shorter file into memory, into a dictionary keyed on the LOGRECNO row:

import csv

with open('sample_state_census.csv', 'rb') as census_file:
    reader = csv.reader(census_file, delimiter='\t')
    census_header = next(reader, None)  # store header
    census = {row[9]: row for row in reader}

then use this dictionary to match against the geo data, write out matches:

with open('sample_state_geo.csv', 'rb') as geo_file:
    with open('outputfile.csv', 'wd') as outfile:
        reader = csv.reader(geo_file, delimiter='\t')
        geo_header = next(reader, None)  # grab header
        geo_header.pop(6) # no need to list LOGRECNO header twice

        writer = csv.writer(outfile, delimiter='\t')
        writer.writerow(census_header + geo_header)

        for row in reader:
            if row[6] not in census:
                # no census data for this LOGRECNO entry
                continue
            # new row is all of the census data plus all of geo minus column 7
            newrow = census[row[6]] + row[:6] + row[7:]
            writer.writerow(newrow)

This all assumes the census file is not so big as to take up too much memory. If that's the case you'll have to use a database instead (read all data into a SQLite database, match in the same vein agains the geo data).

Answer 2

For merging multiple files (even > 2) based on one or more common columns, one of the best and efficient approaches in python would be to use "brewery". You could even specify what fields need to be considered for merging and what fields need to be saved.

import brewery
from brewery
import ds
import sys

sources = [
    {"file": "grants_2008.csv",
     "fields": ["receiver", "amount", "date"]},
    {"file": "grants_2009.csv",
     "fields": ["id", "receiver", "amount", "contract_number", "date"]},
    {"file": "grants_2010.csv",
     "fields": ["receiver", "subject", "requested_amount", "amount", "date"]}
]

Create list of all fields and add filename to store information

about origin of data records

all_fields = brewery.FieldList(["file"])

Go through source definitions and collect the fields

for source in sources:
    for field in source["fields"]:
        if field not in all_fields:

out = ds.CSVDataTarget("merged.csv")
out.fields = brewery.FieldList(all_fields)
out.initialize()

for source in sources:

    path = source["file"]

# Initialize data source: skip reading of headers
# use XLSDataSource for XLS files
# We ignore the fields in the header, because we have set-up fields
# previously. We need to skip the header row.

    src = ds.CSVDataSource(path,read_header=False,skip_rows=1)

    src.fields = ds.FieldList(source["fields"])

    src.initialize()


    for record in src.records():

   # Add file reference into ouput - to know where the row comes from
    record["file"] = path

        out.append(record)

# Close the source stream

    src.finalize()


cat merged.csv | brewery pipe pretty_printer