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Load And Dump Arrays

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.

[1]:
# first of all, import the LArray library
from larray import *

Loading Arrays - Basic Usage (CSV, Excel, HDF5)

To read an array from a CSV file, you must use the read_csv function:

[2]:
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
[2]:
country  gender\time      2013      2014      2015
Belgium         Male   5472856   5493792   5524068
Belgium       Female   5665118   5687048   5713206
 France         Male  31772665  32045129  32174258
 France       Female  33827685  34120851  34283895
Germany         Male  39380976  39556923  39835457
Germany       Female  41142770  41210540  41362080

To read an array from a sheet of an Excel file, you can use the read_excel function:

[3]:
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
[3]:
country  gender\time    2013    2014    2015
Belgium         Male   64371   64173   62561
Belgium       Female   61235   60841   59713
 France         Male  415762  418721  409145
 France       Female  396581  400607  390526
Germany         Male  349820  366835  378478
Germany       Female  332249  348092  359097

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

Warning: open_excel requires to work on Windows and to have the library xlwings installed.

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:

[4]:
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
[4]:
country  gender\time    2013    2014    2015
Belgium         Male   53908   51579   53631
Belgium       Female   55426   53176   56910
 France         Male  287410  282381  297028
 France       Female  281955  277054  296779
Germany         Male  429645  422225  449512
Germany       Female  464180  446131  475688

Dumping Arrays - Basic Usage (CSV, Excel, HDF5)

To write an array in a CSV file, you must use the to_csv method:

[5]:
# 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:

[6]:
# 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:

[7]:
# 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:

[8]:
# 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.

Warning: The save method must be called at the end of the block defined by the with statement to actually write data in the Excel file, otherwise you will end up with an empty file.

# 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:

[9]:
# save the array population in the file 'population.h5' and associate it with the key 'population'
population.to_hdf('population.h5', 'population')

Specifying Wide VS Narrow format (CSV, Excel)

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

[10]:
# 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
[10]:
country\time      2013      2014      2015
     Belgium  11137974  11180840  11237274
      France  65600350  66165980  66458153
[11]:
# same for the read_excel function
population_BE_FR = read_excel(filepath_excel, sheet='population_narrow_format', wide=False)
population_BE_FR
[11]:
country\time      2013      2014      2015
     Belgium  11137974  11180840  11237274
      France  65600350  66165980  66458153

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

[12]:
# 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')
[13]:
# 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 | population |
# | ------- | ---- | ---------- |
# | Belgium | 2013 | 11137974   |
# | Belgium | 2014 | 11180840   |
# | Belgium | 2015 | 11237274   |
# | France  | 2013 | 65600350   |
# | France  | 2014 | 65942267   |
# | France  | 2015 | 66456279   |

Specifying Position in Sheet (Excel)

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.

Warning: Note that the range argument is only available if you have the library xlwings installed (Windows).

# 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

Warning: Note that the position argument is only available if you have the library xlwings installed (Windows).

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

Exporting data without headers (Excel)

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

Specifying the Number of Axes at Reading (CSV, Excel)

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:

[14]:
# 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
[14]:
6 x 4
 country [6]: 'Belgium' 'Belgium' 'France' 'France' 'Germany' 'Germany'
 {1} [4]: 'gender' '2013' '2014' '2015'
dtype: object
memory used: 192 bytes
[15]:
# 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
[15]:
3 x 2 x 3
 country [3]: 'Belgium' 'France' 'Germany'
 gender [2]: 'Male' 'Female'
 time [3]: 2013 2014 2015
dtype: int64
memory used: 144 bytes
[16]:
# 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
[16]:
3 x 2 x 3
 country [3]: 'Belgium' 'France' 'Germany'
 gender [2]: 'Male' 'Female'
 time [3]: 2013 2014 2015
dtype: int64
memory used: 144 bytes

NaNs and Missing Data Handling at Reading (CSV, Excel)

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.

[17]:
# 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')
[17]:
country  gender\time        2013        2014        2015
Belgium         Male   5472856.0   5493792.0   5524068.0
Belgium       Female   5665118.0   5687048.0   5713206.0
 France         Male         nan         nan         nan
 France       Female  33827685.0  34120851.0  34283895.0
Germany         Male  39380976.0  39556923.0  39835457.0
Germany       Female         nan         nan         nan

However, it is possible to choose which value to use to fill missing cells using the fill_value argument:

[18]:
read_csv(csv_dir / 'population_missing_values.csv', fill_value=0)
[18]:
country  gender\time      2013      2014      2015
Belgium         Male   5472856   5493792   5524068
Belgium       Female   5665118   5687048   5713206
 France         Male         0         0         0
 France       Female  33827685  34120851  34283895
Germany         Male  39380976  39556923  39835457
Germany       Female         0         0         0
[19]:
# same for the read_excel function
read_excel(filepath_excel, sheet='population_missing_values', fill_value=0)
[19]:
country  gender\time      2013      2014      2015
Belgium         Male   5472856   5493792   5524068
Belgium       Female   5665118   5687048   5713206
 France         Male         0         0         0
 France       Female  33827685  34120851  34283895
Germany         Male  39380976  39556923  39835457
Germany       Female         0         0         0

Sorting Axes at Reading (CSV, Excel, HDF5)

The sort_rows and sort_columns arguments of the reading functions allows you to sort rows and columns alphabetically:

[20]:
# sort labels at reading --> Male and Female labels are inverted
read_csv(csv_dir / 'population.csv', sort_rows=True)
[20]:
country  gender\time      2013      2014      2015
Belgium       Female   5665118   5687048   5713206
Belgium         Male   5472856   5493792   5524068
 France       Female  33827685  34120851  34283895
 France         Male  31772665  32045129  32174258
Germany       Female  41142770  41210540  41362080
Germany         Male  39380976  39556923  39835457
[21]:
read_excel(filepath_excel, sheet='births', sort_rows=True)
[21]:
country  gender\time    2013    2014    2015
Belgium       Female   61235   60841   59713
Belgium         Male   64371   64173   62561
 France       Female  396581  400607  390526
 France         Male  415762  418721  409145
Germany       Female  332249  348092  359097
Germany         Male  349820  366835  378478
[22]:
read_hdf(filepath_hdf, key='deaths').sort_labels()
[22]:
country  gender\time    2013    2014    2015
Belgium       Female   55426   53176   56910
Belgium         Male   53908   51579   53631
 France       Female  281955  277054  296779
 France         Male  287410  282381  297028
Germany       Female  464180  446131  475688
Germany         Male  429645  422225  449512

Metadata (HDF5)

It is possible to add metadata to arrays:

[23]:
population.meta.title = 'Population at 1st January'
population.meta.origin = 'Table demo_jpan from Eurostat'

population.info
[23]:
title: Population at 1st January
origin: Table demo_jpan from Eurostat
3 x 2 x 3
 country [3]: 'Belgium' 'France' 'Germany'
 gender [2]: 'Male' 'Female'
 time [3]: 2013 2014 2015
dtype: int64
memory used: 144 bytes

These metadata are automatically saved and loaded when working with the HDF5 file format:

[24]:
population.to_hdf('population.h5', 'population')

new_population = read_hdf('population.h5', 'population')
new_population.info
[24]:
title: Population at 1st January
origin: Table demo_jpan from Eurostat
3 x 2 x 3
 country [3]: 'Belgium' 'France' 'Germany'
 gender [2]: 'Male' 'Female'
 time [3]: 2013 2014 2015
dtype: int64
memory used: 144 bytes

Warning: Currently, metadata associated with arrays cannot be saved and loaded when working with CSV and Excel files. This restriction does not apply however to metadata associated with sessions.