The LArray library provides methods and functions to load and dump Array, Session, Axis Group objects to several formats such as Excel, CSV and HDF5. The HDF5 file format is designed to store and organize large amounts of data. It allows to read and write data much faster than when working with CSV and Excel files.
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# first of all, import the LArray library
from larray import *
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csv_dir = get_example_filepath('examples')
# read the array population from the file 'population.csv'.
# The data of the array below is derived from a subset of the demo_pjan table from Eurostat
population = read_csv(csv_dir + '/population.csv')
population
To read an array from a sheet of an Excel file, you can use the read_excel function:
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filepath_excel = get_example_filepath('examples.xlsx')
# read the array from the sheet 'births' of the Excel file 'examples.xlsx'
# The data of the array below is derived from a subset of the demo_fasec table from Eurostat
births = read_excel(filepath_excel, 'births')
births
The open_excel function in combination with the load method allows you to load several arrays from the same Workbook without opening and closing it several times:
# open the Excel file 'population.xlsx' and let it opened as long as you keep the indent.
# The Python keyword ``with`` ensures that the Excel file is properly closed even if an error occurs
with open_excel(filepath_excel) as wb:
# load the array 'population' from the sheet 'population'
population = wb['population'].load()
# load the array 'births' from the sheet 'births'
births = wb['births'].load()
# load the array 'deaths' from the sheet 'deaths'
deaths = wb['deaths'].load()
# the Workbook is automatically closed when getting out the block defined by the with statement
The HDF5 file format is specifically designed to store and organize large amounts of data.
Reading and writing data in this file format is much faster than with CSV or Excel.
An HDF5 file can contain multiple arrays, each array being associated with a key.
To read an array from an HDF5 file, you must use the read_hdf function and provide the key associated with the array:
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filepath_hdf = get_example_filepath('examples.h5')
# read the array from the file 'examples.h5' associated with the key 'deaths'
# The data of the array below is derived from a subset of the demo_magec table from Eurostat
deaths = read_hdf(filepath_hdf, 'deaths')
deaths
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# save the array population in the file 'population.csv'
population.to_csv('population.csv')
To write an array to a sheet of an Excel file, you can use the to_excel method:
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# save the array population in the sheet 'population' of the Excel file 'population.xlsx'
population.to_excel('population.xlsx', 'population')
Note that to_excel create a new Excel file if it does not exist yet.
If the file already exists, a new sheet is added after the existing ones if that sheet does not already exists:
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# add a new sheet 'births' to the file 'population.xlsx' and save the array births in it
births.to_excel('population.xlsx', 'births')
To reset an Excel file, you simply need to set the overwrite_file argument as True:
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# 1. reset the file 'population.xlsx' (all sheets are removed)
# 2. create a sheet 'population' and save the array population in it
population.to_excel('population.xlsx', 'population', overwrite_file=True)
The open_excel function in combination with the dump() method allows you to open a Workbook and to export several arrays at once. If the Excel file doesn't exist, the overwrite_file argument must be set to True.
# to create a new Excel file, argument overwrite_file must be set to True
with open_excel('population.xlsx', overwrite_file=True) as wb:
# add a new sheet 'population' and dump the array population in it
wb['population'] = population.dump()
# add a new sheet 'births' and dump the array births in it
wb['births'] = births.dump()
# add a new sheet 'deaths' and dump the array deaths in it
wb['deaths'] = deaths.dump()
# actually write data in the Workbook
wb.save()
# the Workbook is automatically closed when getting out the block defined by the with statement
To write an array in an HDF5 file, you must use the to_hdf function and provide the key that will be associated with the array:
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# save the array population in the file 'population.h5' and associate it with the key 'population'
population.to_hdf('population.h5', 'population')
By default, all reading functions assume that arrays are stored in the wide format, meaning that their last axis is represented horizontally:
| country \ time | 2013 | 2014 | 2015 |
|---|---|---|---|
| Belgium | 11137974 | 11180840 | 11237274 |
| France | 65600350 | 65942267 | 66456279 |
By setting the wide argument to False, reading functions will assume instead that arrays are stored in the narrow format, i.e. one column per axis plus one value column:
| country | time | value |
|---|---|---|
| Belgium | 2013 | 11137974 |
| Belgium | 2014 | 11180840 |
| Belgium | 2015 | 11237274 |
| France | 2013 | 65600350 |
| France | 2014 | 65942267 |
| France | 2015 | 66456279 |
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# set 'wide' argument to False to indicate that the array is stored in the 'narrow' format
population_BE_FR = read_csv(csv_dir + '/population_narrow_format.csv', wide=False)
population_BE_FR
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# same for the read_excel function
population_BE_FR = read_excel(filepath_excel, sheet='population_narrow_format', wide=False)
population_BE_FR
By default, writing functions will set the name of the column containing the data to 'value'. You can choose the name of this column by using the value_name argument. For example, using value_name='population' you can export the previous array as:
| country | time | population |
|---|---|---|
| Belgium | 2013 | 11137974 |
| Belgium | 2014 | 11180840 |
| Belgium | 2015 | 11237274 |
| France | 2013 | 65600350 |
| France | 2014 | 65942267 |
| France | 2015 | 66456279 |
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# dump the array population_BE_FR in a narrow format (one column per axis plus one value column).
# By default, the name of the column containing data is set to 'value'
population_BE_FR.to_csv('population_narrow_format.csv', wide=False)
# same but replace 'value' by 'population'
population_BE_FR.to_csv('population_narrow_format.csv', wide=False, value_name='population')
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# same for the to_excel method
population_BE_FR.to_excel('population.xlsx', 'population_narrow_format', wide=False, value_name='population')
Like with the to_excel method, it is possible to export arrays in a narrow format using open_excel.
To do so, you must set the wide argument of the dump method to False:
with open_excel('population.xlsx') as wb:
# dump the array population_BE_FR in a narrow format:
# one column per axis plus one value column.
# Argument value_name can be used to change the name of the
# column containing the data (default name is 'value')
wb['population_narrow_format'] = population_BE_FR.dump(wide=False, value_name='population')
# don't forget to call save()
wb.save()
# in the sheet 'population_narrow_format', data is written as:
# | country | time | value |
# | ------- | ---- | -------- |
# | Belgium | 2013 | 11137974 |
# | Belgium | 2014 | 11180840 |
# | Belgium | 2015 | 11237274 |
# | France | 2013 | 65600350 |
# | France | 2014 | 65942267 |
# | France | 2015 | 66456279 |
If you want to read an array from an Excel sheet which does not start at cell A1 (when there is more than one array stored in the same sheet for example), you will need to use the range argument.
# the 'range' argument must be used to load data not starting at cell A1.
# This is useful when there is several arrays stored in the same sheet
births = read_excel(filepath_excel, sheet='population_births_deaths', range='A9:E15')
Using open_excel, ranges are passed in brackets:
with open_excel(filepath_excel) as wb:
# store sheet 'population_births_deaths' in a temporary variable sh
sh = wb['population_births_deaths']
# load the array population from range A1:E7
population = sh['A1:E7'].load()
# load the array births from range A9:E15
births = sh['A9:E15'].load()
# load the array deaths from range A17:E23
deaths = sh['A17:E23'].load()
# the Workbook is automatically closed when getting out the block defined by the with statement
When exporting arrays to Excel files, data is written starting at cell A1 by default. Using the position argument of the to_excel method, it is possible to specify the top left cell of the dumped data. This can be useful when you want to export several arrays in the same sheet for example
filename = 'population.xlsx'
sheetname = 'population_births_deaths'
# save the arrays population, births and deaths in the same sheet 'population_births_and_deaths'.
# The 'position' argument is used to shift the location of the second and third arrays to be dumped
population.to_excel(filename, sheetname)
births.to_excel(filename, sheetname, position='A9')
deaths.to_excel(filename, sheetname, position='A17')
Using open_excel, the position is passed in brackets (this allows you to also add extra informations):
with open_excel('population.xlsx') as wb:
# add a new sheet 'population_births_deaths' and write 'population' in the first cell
# note: you can use wb['new_sheet_name'] = '' to create an empty sheet
wb['population_births_deaths'] = 'population'
# store sheet 'population_births_deaths' in a temporary variable sh
sh = wb['population_births_deaths']
# dump the array population in sheet 'population_births_deaths' starting at cell A2
sh['A2'] = population.dump()
# add 'births' in cell A10
sh['A10'] = 'births'
# dump the array births in sheet 'population_births_deaths' starting at cell A11
sh['A11'] = births.dump()
# add 'deaths' in cell A19
sh['A19'] = 'deaths'
# dump the array deaths in sheet 'population_births_deaths' starting at cell A20
sh['A20'] = deaths.dump()
# don't forget to call save()
wb.save()
# the Workbook is automatically closed when getting out the block defined by the with statement
For some reasons, you may want to export only the data of an array without axes. For example, you may want to insert a new column containing extra information. As an exercise, let us consider we want to add the capital city for each country present in the array containing the total population by country:
| country | capital city | 2013 | 2014 | 2015 |
|---|---|---|---|---|
| Belgium | Brussels | 11137974 | 11180840 | 11237274 |
| France | Paris | 65600350 | 65942267 | 66456279 |
| Germany | Berlin | 80523746 | 80767463 | 81197537 |
Assuming you have prepared an excel sheet as below:
| country | capital city | 2013 | 2014 | 2015 |
|---|---|---|---|---|
| Belgium | Brussels | |||
| France | Paris | |||
| Germany | Berlin |
you can then dump the data at right place by setting the header argument of to_excel to False and specifying the position of the data in sheet:
population_by_country = population.sum('gender')
# export only the data of the array population_by_country starting at cell C2
population_by_country.to_excel('population.xlsx', 'population_by_country', header=False, position='C2')
Using open_excel, you can easily prepare the sheet and then export only data at the right place by either setting the header argument of the dump method to False or avoiding to call dump:
with open_excel('population.xlsx') as wb:
# create new empty sheet 'population_by_country'
wb['population_by_country'] = ''
# store sheet 'population_by_country' in a temporary variable sh
sh = wb['population_by_country']
# write extra information (description)
sh['A1'] = 'Population at 1st January by country'
# export column names
sh['A2'] = ['country', 'capital city']
sh['C2'] = population_by_country.time.labels
# export countries as first column
sh['A3'].options(transpose=True).value = population_by_country.country.labels
# export capital cities as second column
sh['B3'].options(transpose=True).value = ['Brussels', 'Paris', 'Berlin']
# export only data of population_by_country
sh['C3'] = population_by_country.dump(header=False)
# or equivalently
sh['C3'] = population_by_country
# don't forget to call save()
wb.save()
# the Workbook is automatically closed when getting out the block defined by the with statement
By default, read_csv and read_excel will search the position of the first cell containing the special character \ in the header line in order to determine the number of axes of the array to read. The special character \ is used to separate the name of the two last axes. If there is no special character \, read_csv and read_excel will consider that the array to read has only one dimension. For an array stored as:
| country | gender \ time | 2013 | 2014 | 2015 |
|---|---|---|---|---|
| Belgium | Male | 5472856 | 5493792 | 5524068 |
| Belgium | Female | 5665118 | 5687048 | 5713206 |
| France | Male | 31772665 | 31936596 | 32175328 |
| France | Female | 33827685 | 34005671 | 34280951 |
| Germany | Male | 39380976 | 39556923 | 39835457 |
| Germany | Female | 41142770 | 41210540 | 41362080 |
read_csv and read_excel will find the special character \ in the second cell meaning it expects three axes (country, gender and time).
Sometimes, you need to read an array for which the name of the last axis is implicit:
| country | gender | 2013 | 2014 | 2015 |
|---|---|---|---|---|
| Belgium | Male | 5472856 | 5493792 | 5524068 |
| Belgium | Female | 5665118 | 5687048 | 5713206 |
| France | Male | 31772665 | 31936596 | 32175328 |
| France | Female | 33827685 | 34005671 | 34280951 |
| Germany | Male | 39380976 | 39556923 | 39835457 |
| Germany | Female | 41142770 | 41210540 | 41362080 |
For such case, you will have to inform read_csv and read_excel of the number of axes of the output array by setting the nb_axes argument:
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# read the 3 x 2 x 3 array stored in the file 'population_missing_axis_name.csv' wihout using 'nb_axes' argument.
population = read_csv(csv_dir + '/population_missing_axis_name.csv')
# shape and data type of the output array are not what we expected
population.info
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# by setting the 'nb_axes' argument, you can indicate to read_csv the number of axes of the output array
population = read_csv(csv_dir + '/population_missing_axis_name.csv', nb_axes=3)
# give a name to the last axis
population = population.rename(-1, 'time')
# shape and data type of the output array are what we expected
population.info
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# same for the read_excel function
population = read_excel(filepath_excel, sheet='population_missing_axis_name', nb_axes=3)
population = population.rename(-1, 'time')
population.info
Sometimes, there is no data available for some label combinations. In the example below, the rows corresponding to France - Male and Germany - Female are missing:
| country | gender \ time | 2013 | 2014 | 2015 |
|---|---|---|---|---|
| Belgium | Male | 5472856 | 5493792 | 5524068 |
| Belgium | Female | 5665118 | 5687048 | 5713206 |
| France | Female | 33827685 | 34005671 | 34280951 |
| Germany | Male | 39380976 | 39556923 | 39835457 |
By default, read_csv and read_excel will fill cells associated with missing label combinations with nans.
Be aware that, in that case, an int array will be converted to a float array.
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# by default, cells associated will missing label combinations are filled with nans.
# In that case, the output array is converted to a float array
read_csv(csv_dir + '/population_missing_values.csv')
However, it is possible to choose which value to use to fill missing cells using the fill_value argument:
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read_csv(csv_dir + '/population_missing_values.csv', fill_value=0)
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# same for the read_excel function
read_excel(filepath_excel, sheet='population_missing_values', fill_value=0)
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# sort labels at reading --> Male and Female labels are inverted
read_csv(csv_dir + '/population.csv', sort_rows=True)
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read_excel(filepath_excel, sheet='births', sort_rows=True)
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read_hdf(filepath_hdf, key='deaths').sort_axes()
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population.meta.title = 'Population at 1st January'
population.meta.origin = 'Table demo_jpan from Eurostat'
population.info
These metadata are automatically saved and loaded when working with the HDF5 file format:
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population.to_hdf('population.h5', 'population')
new_population = read_hdf('population.h5', 'population')
new_population.info