def export_cells_not_in(inTable, noTable, outTable, inSheet, noSheet, inFID,
noFID):
"""
Export to a new file the cells of in Table not in noTable
"""
import xlrd
import xlwt
from gasp.fm import tbl_to_obj
from gasp.pyt.xls.fld import col_name, get_columns_position
from gasp.pyt.xls.summ import list_unique_values_column
# TODO: check if tables are xls
# Get Data
inData = tbl_to_obj(inTable, sheet=inSheet, output='array')
COLUMNS = col_name(inTable, sheet_name=inSheet)
# From noDATA, get IDS that will not be in the outTable
noXls = xlrd.open_workbook(noTable)
_noSheet = noXls.sheet_by_name(noSheet)
colsPosition = get_columns_position(_noSheet, noFID)
noFIDS = list_unique_values_column(_noSheet, colsPosition[noFID])
# Create Output
out_xls = xlwt.Workbook()
new_sheet = out_xls.add_sheet(inSheet)
# Write columns titles
for c in range(len(COLUMNS)):
new_sheet.write(0, c, COLUMNS[c])
# Write data not in noData
l = 1
for row in inData:
if row[inFID] not in noFIDS:
c = 0
for col in COLUMNS:
new_sheet.write(l, c, row[col])
c += 1
l += 1
out_xls.save(outTable)
return outTable
def groups_to_lines(xls_data, xls_sheet,
group_column, name_colum,
cls_column, interest_column,
column_labels, xls_output):
"""
Table:
| group | label | class | interest
0 | AT | ATLA1 | 0 | 50
1 | AT | ATLA2 | 1 | 70
2 | AT | ATLA3 | 2 | 80
3 | AT | ATLA4 | 2 | 90
4 | AT | ATLA5 | 2 | 10
5 | PT | PTLA1 | 0 | 20
6 | PT | PTLA2 | 2 | 30
7 | PT | PTLA3 | 2 | 40
8 | PT | PTLA4 | 2 | 52
9 | PT | PTLA5 | 1 | 61
10| PT | PTLA6 | 2 | 12
Will be converted to a python object such as:
table = {
AT: {
group_name : ATLA1,
group_value : interest of line 0,
master_unit_value : interest of line 1,
units_value : [interest of line 2, 3, 4]
},
PT: {
group_name : PTLA1,
group_value : interest of line 5,
master_unit_value : interest of line 9,
units_value : [interest of line 6, 7, 8, 10],
}
}
Using this python object, the output table will be write as:
| GROUP_NAME | GROUP_VALUE | MASTER_UNIT_VALUE | UNITS_1 | ... | UNITS_N
0 | ATLA1 | 50 | 70 | 80 | ... | 10
1 | PTLA1 | 20 | 61 | 30 | ... | 12
"""
from gasp.pyt.xls.fld import get_columns_position
interest_columns = [group_column, cls_column, name_colum, interest_column]
# Open input
__xls_data = xlrd.open_workbook(xls_data)
sheet = __xls_data.sheet_by_name(xls_sheet)
# Get columns position
"""
cols_position = {
column_name: position_index, ...
}
"""
cols_position = get_columns_position(sheet, interest_columns)
if interest_column not in cols_position.keys():
return "Interest column not encountered in data file"
# Get data of spreadsheet for the interest column
"""
data = {
group: {
group_name : name,
group_value : value,
master_unit_value : value,
units_value : [value_unit1, ..., value_unitn]
},
}
"""
data = {}
groups_name = {}
for row in range(1, sheet.nrows):
group_id = str(sheet.cell(row, cols_position[group_column]).value)
row_class = int(sheet.cell(row, cols_position[cls_column]).value)
row_name = sheet.cell(row, cols_position[name_colum]).value
row_value = sheet.cell(row, cols_position[interest_column]).value
if group_id not in data.keys():
groups_name[row_name] = group_id
if row_class == 0:
data[group_id] = {
'group_name': row_name,
'group_value': row_value,
'units_value': []
}
elif row_class == 1:
data[group_id] = {
'master_unit_value': row_value,
'units_value': []
}
elif row_class == 2:
data[group_id] = {
'units_value': []
}
else:
return 'Class value not recognized'
else:
if row_class == 0:
groups_name[row_name] = group_id
data[group_id].update({
'group_name': row_name,
'group_value': row_value
})
elif row_class == 1:
data[group_id].update({
'master_unit_value': row_value,
})
elif row_class == 2:
data[group_id]['units_value'].append(row_value)
else:
return 'Class value not recognized'
# Write Output
out_xls = xlwt.Workbook()
out_sheet = out_xls.add_sheet(interest_column)
# Write first line
col_to_write = 0
for k in data:
c = len(data[k]['units_value'])
if c > col_to_write:
col_to_write = c
for i in range(col_to_write):
out_sheet.write(0, i+3, i+1)
# Write second line
for i in range(len(column_labels)):
out_sheet.write(1, i, column_labels[i])
aux_titles = ['Min', 'Max', 'EU28_Average']
for i in range(len(aux_titles)):
out_sheet.write(1, col_to_write + 3 + i, aux_titles[i])
# Organize groups alphabetically based on names
grp_names = sorted(groups_name.keys())
groups = []
for name in grp_names:
groups.append(groups_name[name])
# Write data
l = []
for k in range(len(groups)):
out_sheet.write(k+2, 0, data[groups[k]]['group_name'])
out_sheet.write(k+2, 1, data[groups[k]]['group_value'])
if 'master_unit_value' not in data[groups[k]]:
out_sheet.write(k+2, 2, data[groups[k]]['group_value'])
master_unit_key = 'group_value'
else:
out_sheet.write(k+2, 2, data[groups[k]]['master_unit_value'])
master_unit_key = 'master_unit_value'
for i in range(len(data[groups[k]]['units_value'])):
out_sheet.write(k+2, i+3, data[groups[k]]['units_value'][i])
t = data[groups[k]]['units_value'] + [data[groups[k]][master_unit_key]]
l += t
# Write global min and max
n_l = []
for i in l:
if type(i) == type(10) or type(i) == type(10.0):
n_l.append(i)
out_sheet.write(2, col_to_write + 3, min(n_l))
out_sheet.write(2, col_to_write + 4, max(n_l))
out_xls.save(xls_output)
def split_col_by_year(dataFile, sheetName, codeCol, yearCol, out_file):
"""
For a table:
| code | year | U | LTU
0 | PT1 | 2000 | x | x
1 | PT2 | 2000 | x | x
2 | PT3 | 2000 | x | x
3 | PT1 | 2001 | x | x
4 | PT2 | 2001 | x | x
5 | PT3 | 2001 | x | x
6 | PT1 | 2002 | x | x
7 | PT2 | 2002 | x | x
8 | PT3 | 2002 | x | x
9 | PT1 | 2003 | x | x
10 | PT2 | 2003 | x | x
11 | PT3 | 2003 | x | x
12 | PT1 | 2004 | x | x
13 | PT2 | 2004 | x | x
14 | PT3 | 2004 | x | x
The output will be:
| code | U_2000 | U_2001 | ... | LTU_2000 | ... | LTU_2004
0 | PT1 | x | x | ... | x | ... | x
1 | PT2 | x | x | ... | x | ... | x
2 | PT3 | x | x | ... | x | ... | x
Outfile should be a CSV.
"""
import codecs
from gasp.pyt.xls.fld import get_columns_position, get_columns_position_outside_options
from gasp.pyt.xls.summ import list_unique_values_column
# Open input
xls = xlrd.open_workbook(dataFile)
sheet = xls.sheet_by_name(sheetName)
# Get code and year columns position
codePos_yearPos = get_columns_position(sheet, [codeCol, yearCol])
codePos, yearPos = (codePos_yearPos[codeCol], codePos_yearPos[yearCol])
# Get variable positions
varPos = get_columns_position_outside_options(sheet, [codeCol, yearCol])
# Get data
data = {}
VARS = []
YEARS = []
for row in range(1, sheet.nrows):
entity = sheet.cell(row, codePos).value
year = int(sheet.cell(row, yearPos).value)
if year not in YEARS:
YEARS.append(year)
if entity not in data:
data[entity] = {}
if year not in data[entity]:
data[entity][year] = {}
for var in varPos:
if var not in data[entity][year]:
data[entity][year][var] = sheet.cell(row, varPos[var]).value
else:
print('Variable is already there for that entity and year')
if var not in VARS:
VARS.append(var)
VARS.sort()
YEARS.sort()
with codecs.open(out_file, mode='w', encoding='utf-8') as f:
# Write titles
f.write(codeCol + '\t')
for var in VARS:
for year in YEARS:
f.write('{v}_{y}\t'.format(v=str(var), y=str(year)))
f.write('\n')
# Write real data
for entity in data:
f.write(entity + '\t')
for var in VARS:
for year in YEARS:
if year in data[entity]:
f.write('{}\t'.format(str(data[entity][year][var])))
else:
f.write('\t')
f.write('\n')
f.close()
"""
def transpose_groups_in_sheets(excel, datasheet, group, _id, output):
"""
For an Input Table like:
| group | id | variable_1 | variable_2
0 | A | 1 | V1_A1 | V2_A1
1 | A | 2 | V1_A2 | V2_A2
2 | B | 1 | V1_B1 | V2_B1
3 | B | 2 | V1_B2 | V2_B2
This method will generate one table for each group:
* GROUP A
| variables | var_code | id1_value | id2_value
0 | variable_1 | | V1_A1 | V1_A2
1 | variable_2 | | V2_A1 | V2_A2
* GROUP B
| variables | var_code | id1_value | id2_value
0 | variable_1 | | V1_B1 | V1_B2
1 | variable_2 | | V2_B1 | V2_B2
"""
import xlrd; import xlwt
from gasp.pyt.xls.fld import get_columns_position
from gasp.pyt.xls.fld import get_columns_position_outside_options
# Get data from excel
xls = xlrd.open_workbook(excel)
sdata = xls.sheet_by_name(datasheet)
# Get data by group and variable
"""
data = {
groupA : {
varA : [val_1, ..., val_n],
varB : [val_1, ..., val_n]
},
...
groupN: {
varA : [val_1, ..., val_n],
varB : [val_1, ..., val_n]
}
}
"""
position_grp_id = get_columns_position(sdata, [group, _id])
position_var = get_columns_position_outside_options(sdata, [group, _id])
# To mantain the writting order of the variables (first column
# should be first line.)
var_cols = position_var.values()
var_cols.sort()
for i in range(len(var_cols)):
for col in position_var:
if var_cols[i] == position_var[col]:
var_cols[i] = col
# The columns names will be written in the correct order
groupsChild = {}
for row in range(1, sdata.nrows):
grp = sdata.cell(row, position_grp_id[group]).value
if grp not in groupsChild:
groupsChild[grp] = {}
entity = sdata.cell(row, position_grp_id[_id]).value
for col in var_cols:
value = sdata.cell(row, position_var[col]).value
if not col in groupsChild[grp]:
groupsChild[grp][col] = [value]
else:
groupsChild[grp][col].append(value)
# Write output - one table for each group
out_xls = xlwt.Workbook()
for grp in groupsChild:
grp_sheet = out_xls.add_sheet(grp)
# Write first line
titles = ['MIN', 'Max',
'component/area of concern/dimension']
for i in range(len(groupsChild[grp][groupsChild[grp].keys()[0]])):
titles.append(i+1)
for i in range(len(titles)):
grp_sheet.write(0, i, titles[i])
# Write Values
for i in range(len(var_cols)): # each iteration is a new line
# Write Variable name
grp_sheet.write(i+1, 2, var_cols[i])
# Write entities values
for e in range(len(groupsChild[grp][var_cols[i]])):
grp_sheet.write(i+1, 2 + e + 1,
groupsChild[grp][var_cols[i]][e])
# Write MIN and MAX
grp_sheet.write(i+1, 0, min(groupsChild[grp][var_cols[i]]))
grp_sheet.write(i+1, 1, max(groupsChild[grp][var_cols[i]]))
out_xls.save(output)
return output
def year_to_col_by_attr(data_file, data_spreadsheet,
field_id,
fields_to_mantain,
field_year,
output_file):
"""
Transpose a table by attribute and year
For a given table such as:
| group | fid | label | year | attribute_x | attribute_y
0 | AT | AT_01 | ATLA1 | 2000 | val_x | val_y
1 | AT | AT_01 | ATLA1 | 2001 | val_x | val_y
2 | AT | AT_02 | ATLA2 | 2000 | val_x | val_y
3 | AT | AT_02 | ATLA2 | 2001 | val_x | val_y
4 | AT | AT_03 | ATLA3 | 2000 | val_x | val_y
5 | AT | AT_03 | ATLA3 | 2001 | val_x | val_y
6 | AT | AT_04 | ATLA4 | 2000 | val_x | val_y
7 | AT | AT_04 | ATLA4 | 2001 | val_x | val_y
8 | PT | PT_01 | PTLA1 | 2000 | val_x | val_y
9 | PT | PT_01 | PTLA1 | 2001 | val_x | val_y
10 | PT | PT_02 | PTLA2 | 2000 | val_x | val_y
11 | PT | PT_02 | PTLA2 | 2001 | val_x | val_y
12 | PT | PT_03 | PTLA3 | 2000 | val_x | val_y
13 | PT | PT_03 | PTLA3 | 2001 | val_x | val_y
This method will generate one table (spreadsheet) for each attribute:
Table attribute_x
| group | fid | label | 2000 | 2001
0 | AT | AT_01 | ATLA1 | val_x | val_x
1 | AT | AT_02 | ATLA2 | val_x | val_x
2 | AT | AT_03 | ATLA3 | val_x | val_x
3 | AT | AT_04 | ATLA4 | val_x | val_x
4 | PT | PT_01 | PTLA1 | val_x | val_x
5 | PT | PT_02 | PTLA2 | val_x | val_x
6 | PT | PT_03 | PTLA3 | val_x | val_x
Table attribute_y
| group | fid | label | 2000 | 2001
0 | AT | AT_01 | ATLA1 | val_y | val_y
1 | AT | AT_02 | ATLA2 | val_y | val_y
2 | AT | AT_03 | ATLA3 | val_y | val_y
3 | AT | AT_04 | ATLA4 | val_y | val_y
4 | PT | PT_01 | PTLA1 | val_y | val_y
5 | PT | PT_02 | PTLA2 | val_y | val_y
6 | PT | PT_03 | PTLA3 | val_y | val_y
"""
from gasp.pyt.xls.fld import get_columns_position
from gasp.pyt.xls.fld import get_columns_position_outside_options
from gasp.pyt.xls.summ import list_unique_values_column
from gasp.pyt.xls.fm import get_unit_attributes
# Open input
xls_data = xlrd.open_workbook(data_file)
sheet = xls_data.sheet_by_name(data_spreadsheet)
# Get interest columns position
interest_position = get_columns_position(sheet, fields_to_mantain)
# Get year position
year_position = get_columns_position(sheet, [field_year])
# Get id position
id_position = get_columns_position(sheet, [field_id])
# Get attributes columns position - indicators
attributes_position = get_columns_position_outside_options(
sheet, fields_to_mantain + [field_year, field_id]
)
# List years
years = list_unique_values_column(sheet, year_position[field_year])
# List entities
units = list_unique_values_column(sheet, id_position[field_id])
units_atributtes = get_unit_attributes(
sheet, id_position[field_id], interest_position
)
# add years to data dict
for unit in units_atributtes:
for year in years:
units_atributtes[unit][year] = {}
# Get values by year for each unit
for line in range(1, sheet.nrows):
__year = sheet.cell(line, year_position[field_year]).value
__id = sheet.cell(line, id_position[field_id]).value
if __id not in units_atributtes.keys():
continue
for indicator in attributes_position:
if __year not in years:
continue
units_atributtes[__id][__year][indicator] = sheet.cell(
line, attributes_position[indicator]).value
# Write output
out_xls = xlwt.Workbook()
# for each attribute/indicator column
for indicator in attributes_position:
# Create new sheet
out_sheet = out_xls.add_sheet(indicator)
# Write columns
out_sheet.write(0, 0, field_id)
c = 1
for field in interest_position:
out_sheet.write(0, c, field)
c += 1
for year in years:
out_sheet.write(0, c, year)
c += 1
# Write real data
lnh = 1
for unit in units_atributtes:
out_sheet.write(lnh, 0, unit)
col = 1
for field in interest_position:
out_sheet.write(
lnh,
col,
units_atributtes[unit][field]
)
col += 1
for year in years:
out_sheet.write(
lnh,
col,
units_atributtes[unit][year][indicator]
)
col += 1
lnh += 1
out_xls.save(output_file)
def frequencies_by_place(xls,
entities_sheet,
entities_id,
entities_place,
data_sheet,
output,
null=None,
out_cols_basename=None,
entities_filter=None,
cols_exclusion=None):
"""
Entities table:
ID_Aluno | codigo_postal
xxxxxxxx | xxxx-xxx
xxxxxxxx | xxxx-xxx
data_sheet:
ID_Aluno | disciplina_1 | disciplina_2 | disciplina_n
xxxxxxxx | nota_1 | xxxxxxx | xxxxxxx
xxxxxxxx | nota_2 | xxxxxxx | xxxxxxx
xxxxxxxx | nota_3 | xxxxxxx | xxxxxxx
Output tables (one for each disciplina and other for the total)
| nota_1 | nota_2 | nota_3 | Mean | Variance
total | NrAlunos | NrAlunos | NrAlunos | x | y
codigo_postal_1 | NrAlunos | NrAlunos | NrAlunos | x | y
codigo_postal_n | NrAlunos | NrAlunos | NrAlunos | x | y
In the entities_sheet and data_sheet, there must be only one referente
for an entity (duplicated entities are not allowed)
Filtering entities properties is possible
"""
import numpy
from decimal import Decimal
from gasp.pyt.xls.fld import get_columns_position
from gasp.pyt.xls.summ import list_unique_values_column
from gasp.pyt.xls.summ import count_values_column
from gasp.pyt.xls.summ import count_values_column_if_entity_exists
from gasp.pyt.xls.summ import count_values_column_by_entity_property
"""
Aux method to estimate some generic statistics based on a "Dict Histogram"
(total, mean and standard deviation)
"""
def map_dict(histogram):
if 'mean' in histogram.keys():
del histogram['mean']
if 'stdesviation' in histogram.keys():
del histogram['stdesviation']
if 'total' in histogram.keys():
del histogram['total']
# Get the total of entities
histogram['total'] = sum(histogram.values())
# Get mean
numerator = 0
denominator = 0
std_sample = []
for v in histogram:
if type(v) != str:
numerator += Decimal(v) * Decimal(histogram[v])
denominator += histogram[v]
std_sample += [v for x in range(histogram[v])]
if numerator and denominator:
histogram['mean'] = numerator / Decimal(denominator)
histogram['stdesviation'] = Decimal(numpy.std(std_sample))
return histogram
# Open xls file
__xls_file = xlrd.open_workbook(xls, on_demand=True)
# Map entities with location
"""
d = {
entitie_id : location
}
"""
if entities_filter:
filters = entities_filter.split(';')
filters_map = {}
for flt in filters:
col_filter, val_filter = flt.split('=')
filters_map[col_filter] = val_filter
interest_col = [entities_id, entities_place] + filters_map.keys()
else:
filters_map = 0
interest_col = [entities_id, entities_place]
__entities_sheet = __xls_file.sheet_by_name(entities_sheet)
id_place_position = get_columns_position(__entities_sheet, interest_col)
if entities_id not in id_place_position.keys():
raise ValueError('Couldn\'t find the given column id')
elif entities_place not in id_place_position.keys():
raise ValueError('Couldn\'t find the position of the place id')
entities_map = {}
for row in range(1, __entities_sheet.nrows):
__id = __entities_sheet.cell(row, id_place_position[entities_id]).value
location = __entities_sheet.cell(
row, id_place_position[entities_place]).value
if filters_map:
c = 0
for col_filter in filters_map:
__filter = __entities_sheet.cell(
row, id_place_position[col_filter]).value
if __filter != val_filter:
c += 1
break
if c:
continue
entities_map[__id] = location
# Count Entities by value in each column
"""
d = {
col_1 : {
total : {
total : total_count,
value_1 : value_1_count,
value_2 : value_2_count,
value_n : value_n_count
},
location_1: {
total : total_count,
value_1 : value_1_count,
value_2 : value_2_count,
value_n : value_n_count
},
location_2: {
total : total_count,
value_1 : value_1_count,
value_2 : value_2_count,
value_n : value_n_count
}
},
col_2 : {
total : {
total : total_count,
value_1 : value_1_count,
value_2 : value_2_count,
value_n : value_n_count
},
location_1: {
total : total_count,
value_1 : value_1_count,
value_2 : value_2_count,
value_n : value_n_count
},
location_2: {
total : total_count,
value_1 : value_1_count,
value_2 : value_2_count,
value_n : value_n_count
}
},
col_n : {
...
}
}
"""
data_sheet = __xls_file.sheet_by_name(data_sheet)
data = {}
cols_exclusion = cols_exclusion if type(cols_exclusion) == list else \
[cols_exclusion] if type(cols_exclusion) == str else 0
for col in range(1, data_sheet.ncols):
column_name = data_sheet.cell(0, col).value
if cols_exclusion:
if column_name in cols_exclusion:
continue
# List unique values
values = list_unique_values_column(data_sheet, col)
# Del NoData identifier if defined
if null or null == 0:
if null in values:
values.remove(null)
# Count occourences of a value in that column
val_count = count_values_column_if_entity_exists(
data_sheet, col, entities_map.keys(), values)
for v in values:
if v not in val_count:
val_count[v] = 0
data[column_name] = {'total': map_dict(val_count)}
# Do the same for each location
locations = list(set(entities_map.values()))
for place in locations:
val_count = count_values_column_by_entity_property(
data_sheet, col, place, entities_map, values)
for v in values:
if v not in val_count:
val_count[v] = 0
data[column_name].update({place: map_dict(val_count)})
"""
Update data dict with one combination of all keys/values data
d.update({
general : {
total : {
total : total_count,
value_1 : value_1_count,
value_2 : value_2_count,
value_n : value_n_count
},
location_1: {
total : total_count,
value_1 : value_1_count,
value_2 : value_2_count,
value_n : value_n_count
},
location_2: {
total : total_count,
value_1 : value_1_count,
value_2 : value_2_count,
value_n : value_n_count
}
}
})
"""
general = {}
for col_sheet in data:
for k in data[col_sheet]:
if k not in general:
general[k] = {}
for _k in data[col_sheet][k]:
general[k][_k] = data[col_sheet][k][_k]
else:
for _k in data[col_sheet][k]:
if _k in general[k]:
general[k][_k] += data[col_sheet][k][_k]
else:
general[k][_k] = data[col_sheet][k][_k]
data['general'] = general
for __dict in data['general']:
with_stats = map_dict(data['general'][__dict])
data['general'][__dict] = with_stats
# Write data in a new xls_file
out_xls = xlwt.Workbook()
for col_sheet in data:
new_sheet = out_xls.add_sheet(col_sheet)
if out_cols_basename:
cols_basename = [
'{b}_{_k}'.format(b=out_cols_basename, _k=str(k))
for k in data[col_sheet]['total'].keys()
]
else:
cols_basename = data[col_sheet]['total'].keys()
cols_name = data[col_sheet]['total'].keys()
cols_basename.sort()
cols_name.sort()
# Write columns names
for c in range(len(cols_name)):
new_sheet.write(0, c + 1, cols_basename[c])
# Write lines
lnh = 1
lines_name = data[col_sheet].keys()
lines_name.sort()
for line in lines_name:
# Write line name
new_sheet.write(lnh, 0, line)
# Write counting data
for cln in range(len(cols_name)):
if cols_name[cln] in data[col_sheet][line].keys():
new_sheet.write(lnh, cln + 1,
data[col_sheet][line][cols_name[cln]])
lnh += 1
out_xls.save(output)
__xls_file.release_resources()
del __xls_file
def frequencies_table(xls,
data_sheet,
output,
entities_sheet=None,
entities_id=None,
entities_filter=None,
values_filter=None):
"""
Count values occurences in each attribute column
input table:
entity | attr_1 | attr_2 | ... | attr_n
0 | a | b | ... | a
1 | b | c | ... | b
2 | b | c | ... | c
3 | a | f | ... | d
4 | c | a | ... | f
output table:
| a | b | c | d | f |
attr_1 | 2 | 2 | 1 | 0 | 0 |
attr_2 | 1 | 1 | 2 | 0 | 1 |
attr_n | 1 | 1 | 1 | 1 | 1 |
It is possible filter entities and attribute values
"""
from gasp.pyt.xls.fld import get_columns_position
from gasp.pyt.xls.summ import count_values_column
from gasp.pyt.xls.summ import count_values_column_if_entity_exists
# Open file
__xls = xlrd.open_workbook(xls, on_demand=True)
if entities_sheet and entities_id and entities_filter:
filters = entities_filter.split(';')
filters_map = {}
for flt in filters:
col_filter, val_filter = flt.split('=')
filters_map[col_filter] = val_filter
__entities_sheet = __xls.sheet_by_name(entities_sheet)
int_col = [entities_id] + filters_map.keys()
cols_position = get_columns_position(__entities_sheet, int_col)
entities_map = []
for row in range(1, __entities_sheet.nrows):
__id = __entities_sheet.cell(row, cols_position[entities_id]).value
c = 0
for col_filter in filters_map:
__filter = __entities_sheet.cell(
row, cols_position[col_filter]).value
if __filter != filters_map[col_filter]:
c += 1
break
if c:
continue
entities_map.append(__id)
else:
entities_map = None
attr_sheet = __xls.sheet_by_name(data_sheet)
data = {}
registed_values = []
values_filter = values_filter if type(values_filter) == list else \
[values_filter] if type(values_filter) == str else None
for col in range(1, attr_sheet.ncols):
col_name = attr_sheet.cell(0, col).value
if not entities_map:
values_count = count_values_column(attr_sheet, col, values_filter)
else:
values_count = count_values_column_if_entity_exists(
attr_sheet, col, entities_map, values_filter)
registed_values.extend(values_count.keys())
data[col_name] = values_count
registed_values = list(set(registed_values))
# Write output
out_xls = xlwt.Workbook()
new_sheet = out_xls.add_sheet(
os.path.splitext(os.path.basename(output))[0])
# columns
for i in range(len(registed_values)):
new_sheet.write(0, i + 1, registed_values[i])
# Write values
r = 1
for row in data:
new_sheet.write(r, 0, row)
for col in range(len(registed_values)):
if registed_values[col] in data[row]:
new_sheet.write(r, col + 1, data[row][registed_values[col]])
r += 1
out_xls.save(output)
__xls.release_resources()
del __xls
def frequencies_by_entity_attr(xls,
entities_sheet,
entities_id,
attr_sheet,
interest_values,
output,
entities_filter=None,
attr_exclusion=None):
"""
Count entities with a number of attributes with a set of specific values
Example:
specific_values = A, S
Entities table:
entity_id | some_data
xxxxxxxx | xxxx-xxx
xxxxxxxx | xxxx-xxx
attributes_sheet:
From tables as:
entity | attr_1 | attr_2 | ... | attr_n
0 | S | S | ... | S
1 | A | B | ... | S
2 | B | B | ... | A
3 | S | A | ... | S
4 | A | S | ... | B
We came to the following result:
values | entities_count | attribute_count
S;A | 2 | 3
S;A | 2 | 2
S;A | 1 | 1
Filtering entities properties is possible... Many could be used...
When many fields are used, the AND operator logic will be applied
E.g of application:
Number of students with a number of negatives
"""
from gasp.pyt.xls.fld import get_columns_position
# Open file
__xls = xlrd.open_workbook(xls, on_demand=True)
# Map entities
if entities_filter:
filters = entities_filter.split(';')
filters_map = {}
for flt in filters:
col_filter, val_filter = flt.split('=')
filters_map[col_filter] = val_filter
interest_col = [entities_id] + filters_map.keys()
else:
filters_map = 0
interest_col = entities_id
__entities_sheet = __xls.sheet_by_name(entities_sheet)
cols_position = get_columns_position(__entities_sheet, interest_col)
if entities_id not in cols_position:
raise ValueError('Couldn\'t find the given column id')
entities_map = {}
for row in range(1, __entities_sheet.nrows):
__id = __entities_sheet.cell(row, cols_position[entities_id]).value
if filters_map:
c = 0
for col_filter in filters_map:
__filter = __entities_sheet.cell(
row, cols_position[col_filter]).value
if __filter != filters_map[col_filter]:
c += 1
break
if c:
continue
entities_map[__id] = 0
# Count occurences of the specific/interes values by entitie
data_sheet = __xls.sheet_by_name(attr_sheet)
interest_values = interest_values if type(interest_values) == list else \
[interest_values] if type(interest_values) == str else 0
if not interest_values:
raise ValueError('interest_values should be a list or string')
for col in range(1, data_sheet.ncols):
column_name = data_sheet.cell(0, col).value
if attr_exclusion and type(attr_exclusion) == list:
if column_name in attr_exclusion:
continue
for row in range(1, data_sheet.nrows):
__id = data_sheet.cell(row, 0).value
value = data_sheet.cell(row, col).value
if value in interest_values:
if __id in entities_map:
entities_map[__id] += 1
# Count the number entities with the same number of occurences of the
# interest/specific values
occurence_count = entities_map.values()
occurence_unique = list(set(occurence_count))
entities_countv = {}
for countv in occurence_unique:
entities_count = occurence_count.count(countv)
entities_countv[entities_count] = countv
# Write output
out_xls = xlwt.Workbook()
new_sheet = out_xls.add_sheet(
os.path.splitext(os.path.basename(output))[0])
# colums
c = ['values', 'entities_number', 'values_occurences']
for i in range(len(c)):
new_sheet.write(0, i, c[i])
# values
l = 1
for i in entities_countv:
new_sheet.write(l, 0, ';'.join([str(x) for x in interest_values]))
new_sheet.write(l, 1, i)
new_sheet.write(l, 2, entities_countv[i])
l += 1
out_xls.save(output)
__xls.release_resources()
del __xls
def identify_cells_with_char(xls, sheetname, col_id, col_interest,
interest_char, output):
"""
Identify the cells with a specific character in one column of interest
Record these cells (ID and interest column) in a new file.
"""
import xlrd; import xlwt
from gasp.pyt.xls.fld import get_columns_position
__xls = xlrd.open_workbook(xls)
sheet = __xls.sheet_by_name(sheetname)
cols_position = get_columns_position(sheet, [col_id, col_interest])
out_xls = xlwt.Workbook()
new_sheet = out_xls.add_sheet(sheetname)
new_sheet.write(0, 0, col_id)
new_sheet.write(0, 1, col_interest)
Nrow = 1
for row in range(1, sheet.nrows):
val = sheet.cell(row, cols_position[col_interest]).value
if interest_char in val:
new_sheet.write(Nrow, 0, sheet.cell(row, cols_position[col_id]).value)
new_sheet.write(Nrow, 1, val)
Nrow+=1
def xlstimedelta_to_pddf(inXls,
timecol,
sheet_name=None,
sheet_index=None,
columnsToMantain=None):
"""
Convert a table with a column with timedelta values to a valid
Pandas DataFrame
"""
import datetime
import xlrd
from xlrd import xldate_as_tuple
from gasp.pyt import obj_to_lst
from gasp.pyt.xls.sheet import get_sheet_obj
from gasp.pyt.xls.fld import col_name, get_columns_position
__xls = xlrd.open_workbook(inXls)
sheet = get_sheet_obj(__xls, name=sheet_name, index=sheet_index)
# Get Cols name
COLS_NAME = col_name(sheet) if not columnsToMantain else \
obj_to_lst(columnsToMantain)
if type(timecol) != int:
if timecol not in COLS_NAME:
COLS_NAME.append(timecol)
# Get Cols position
COLS_POS = get_columns_position(sheet, COLS_NAME)
POS_COLS = COLS_POS.values()
if type(timecol) == int:
COL_TIME_POSITION = timecol
else:
COL_TIME_POSITION = COLS_POS[timecol]
data = []
for row in range(1, sheet.nrows):
l_col = []
for col in range(sheet.ncols):
if col not in POS_COLS:
continue
if col == COL_TIME_POSITION:
tt = xldate_as_tuple(
sheet.cell(row, col).value, __xls.datemode)
l_col.append(
datetime.timedelta(weeks=tt[1],
days=tt[2],
hours=tt[3],
minutes=tt[4],
seconds=tt[5]))
else:
l_col.append(sheet.cell(row, col).value)
data.append(l_col)
df = pandas.DataFrame(data, columns=COLS_NAME)
return df
