I am writing an application to create a new blank day's worth of cells in a financial transaction spreadsheet. The spreadsheet was generated by WPS.
Everything is working as I'd like, except that data validation for one column only does not copy successfully from the old cells to the new cells in the same column. When I examine the sheetobj.data_validations, I find that the working columns end like this:
M3217:M65536, E3217:E65536
The column that doesn't work correctly ends like this:
N3196:N3215
Here is the code that actually copies the cells:
for r in range(frod, eod):
for c in range(1, maxc + 1):
nrow = r + rinc
cell_tmp = sheet.cell(row = r, column = c)
val_tmp = cell_tmp.value
new_cell = sheet.cell(row = nrow, column = c)
new_cell.font = copy(cell_tmp.font)
new_cell.fill = copy(cell_tmp.fill)
new_cell.number_format = copy(cell_tmp.number_format)
new_cell.alignment = copy(cell_tmp.alignment)
new_cell.border = copy(cell_tmp.border)
headstr = col_string(sheet.title, c)
method = get_method(headstr)
#print('header string for col', c, 'is', headstr, 'form method is', method)
if cell_tmp.data_type is not 'f':
if method == 'cp_eod' and r == (eod - 2):
new_cell.value = val_tmp
else:
new_cell.value = None
new_cell.data_type = cell_tmp.data_type
elif method == 'tc_eod':
new_cell.value = form_self(val_tmp, r, nrow)
elif method == 'prev_inc':
if r == frod:
new_cell.value = form_inc(val_tmp, val_tmp[val_tmp.rindex('+')+1:])
else:
new_cell.value = date_prev(val_tmp, r, nrow)
elif method == 'self':
if r == eod:
#print('*** EOD ***')
new_cell.value = form_self_dec(val_tmp, r, nrow)
else:
new_cell.value = form_self(val_tmp, r, nrow)
elif method == 'self_fod':
if r == frod:
new_cell.value = form_self(val_tmp, r, nrow)
elif r == eod:
new_cell.value = form_self_dec(val_tmp, r, nrow)
else:
new_cell.value = form_frod(val_tmp, frod, frod + rinc, r, nrow)
elif method == 'self_eod':
new_cell.value = form_eod(val_tmp, r, nrow, eod, eod + rinc)
elif method == 'tcsbs':
new_cell.value = form_tcsbs(val_tmp, val_tmp[val_tmp.rindex('$')+1:])
elif method == 'self_prev':
if r == eod:
new_cell.value = form_prev(val_tmp, nrow)
else:
new_cell.value = form_self_dec(val_tmp, r, nrow)
The column in question falls into this block of code:
else:
new_cell.value = None
new_cell.data_type = cell_tmp.data_type
Is it possible to edit the sheetobj.data_validations for column N to end the same way the working columns do?
Other things I have tried:
Directly setting a new data validation for the cells in the column. Python showed the cells having data validation but when I saved the workbook and opened in google sheets, it was not there.
Copying a cell from a good column into the bad column on the original sheet.
This also did not solve my problem, when I copied a new block of cells, the data validation did not copy.
Copying a cell from a good column in another sheet into the bad column on the original sheet.
This resulted in losing all data validation when I copied a new block of cells.
Related
I'm trying to save dataframes into tabs/sheets in an existing xlsm file. I was able to save multiple sheets into an xlsx file with engine=xlsxwriter but couldn't find a way to modify it to write to a xlsm file. I found numerous examples using openpyxl as an engine but haven't found a way to predefine the cell formats. Here's the xlswriter code:
with pd.ExcelWriter(filename + '.xlsx', engine='xlsxwriter') as writer:
df.to_excel(writer, sheet_name=tabname)
workbook = writer.book
worksheet = writer.sheets[tabname]
fmt0 = workbook.add_format({'num_format': '0'})
fmt1 = workbook.add_format({'num_format': '0.0'})
fmt2 = workbook.add_format({'num_format': '0.00'})
for r in range(len(df.index)):
s = r + 1
cols = {3: 'min', 4: 'max'}
for c in cols:
v = df.at[df.index[r], cols[c]]
if is_number(v):
if '.' not in v:
worksheet.write_number(s, c, float(v), fmt0)
else:
pl = len(v) - v.index('.') - 1 # number of digits after '.'
if pl == 1:
worksheet.write_number(s, c, float(v), fmt1)
elif pl == 2:
worksheet.write_number(s, c, float(v), fmt2)
Does openpyxl have a way to do the same thing?
I think I can define a function that will do this but if there's a way to modify the existing code, I'd rather do that.
with an SQL query, I get a number of customers, then a number of suppliers for each customer.
The objective is to create an Excel workbook per customer and in this workbook, at the rate of one sheet per supplier, copy the details of the supplier's invoices.
The code below allows you to create Excel workbooks and one sheet per supplier, but the copy of invoices per supplier does not work. Where is the error?
My code :
Clients=list(generator_df['Client'].unique())
for i in range(0,len(Clients)):
if len(Clients[i])!= 0:
workbook = xlsxwriter.Workbook(path+'"'+Clients[i]+".xlsx")
Four = generator_df[['Four']][generator_df['Client'] == Clients[i]].dropna()
Fournisseurs = list(Four['Four'].unique())
for j in range(0,len(Fournisseurs)):
if j == 0:
worksheet = workbook.add_worksheet(Fournisseurs[0])
Detail = detail_df[['Num_facture','Date_facture','Code_produit','Designation','Qte','PU_net','Montant_HT']][(detail_df['Fournisseur'] == Fournisseurs[0]) & (detail_df['Client']==Clients[i])].dropna()
Detail.to_excel(path+'"'+Clients[i]+".xlsx", sheet_name = Fournisseurs[0], header = True, index = False)
else:
Num=str(j)
worksheetNum = workbook.add_worksheet(Fournisseurs[j])
Detail_j = detail_df[['Num_facture','Date_facture','Code_produit','Designation','Qte','PU_net','Montant_HT']][(detail_df['Fournisseur'] == Fournisseurs[j]) & (detail_df['Client'] == Clients[i])].dropna()
Detail_1.to_excel(path+'"'+Clients[i]+".xlsx", sheet_name = Fournisseurs[j], header = True, index = False)
workbook.close()
after several modifications of the code, the problem is solved.
"
Clients = list(generator_df['Client'].unique())
for i in range(0, len(Clients)):
if len(Clients[i]) != 0:
writer = pd.ExcelWriter(
path+'"'+Clients[i]+".xlsx", engine='xlsxwriter')
Four = generator_df[['Four']][generator_df['Client']
== Clients[i]].dropna()
Fournisseurs = list(Four['Four'].unique())
for j in range(0, len(Fournisseurs)):
if j == 0:
Detail = detail_df[['Num_facture', 'Date_facture', 'Code_produit',
'Designation', 'Qte', 'PU_net', 'Montant_HT']][(detail_df['Fournisseur']
== Fournisseurs[0]) & (detail_df['Client'] == Clients[i])].dropna()
Detail.to_excel(writer, sheet_name=Fournisseurs[0],
header=True, index=False)
else:
Num = str(j)
Detail_j = detail_df[['Num_facture', 'Date_facture', 'Code_produit',
'Designation', 'Qte', 'PU_net', 'Montant_HT']][(detail_df['Fournisseur'] == Fournisseurs[j]) & (detail_df['Client'] ==
Clients[i])].dropna()
Detail_j.to_excel(writer, sheet_name=Fournisseurs[j],
header=True, index=False)
writer.save()
Im trying to convert xls to xlsx files including cell style. At the moment, Im working with cell fonts.
Is there another way for xlrd to determine whether the cell font is bold or not other than font._font_flag == 1?
def xlstoxlsx():
input_file_loc = [x for x in os.listdir(path1) if x.endswith(".xls")]
count = 0
for file in input_file_loc:
xlsxwb = xlsxwriter.Workbook(f"{path1}/" + input_file_loc[count] + '_Coverted {:2d}.xlsx'.format(count))
xlswb = xlrd.open_workbook(path1 + "/" + file, formatting_info=True)
bold = xlsxwb.add_format({'bold': True})
italic = xlsxwb.add_format({'italic': True})
uline = xlsxwb.add_format({'underline': True})
for sht in xlswb.sheet_names():
xlsws = xlswb[sht]
rows, cols = xlsws.nrows, xlsws.ncols
all_rows = []
for row in range(rows):
curr_row = []
for col in range(cols):
curr_row.append(xlsws.cell_value(row, col))
all_rows.append(curr_row)
xlsxws = xlsxwb.add_worksheet()
for row in range(len(all_rows)):
for col in range(len(all_rows[0])):
cell = xlsws.cell(row, col)
font = xlswb.xf_list[cell.xf_index]
if font._font_flag == 1:
xlsxws.write(row, col, all_rows[row][col], bold)
elif font._font_flag == 0:
xlsxws.write(row, col, all_rows[row][col])
count += 1
xlsxwb.close()
Is there a way to get the maximum per row using eval?
It would be very sweet to be able to write something like:
X = pd.DataFrame({'A':[1,2,3,4]})
X.eval("""B = 2* A
C = A +B
D = max(A, B)
E = 2 * D
""", inplace = True)
Instead of:
X = pd.DataFrame({'A':[1,2,3,4]})
X.eval("""B = 2* A
C = A +B
""", inplace = True)
X['D'] = X.loc[:,['A', 'B']].max(axis=1)
X.eval('E = 2 * D', inplace=True)
EDIT:
As suggested by #mephisto, something similar to this works beautifully:
def rowmax(A,B):
return pd.concat([A,B], axis=1).max(axis=1)
X = pd.DataFrame({'A':[0, 1,2,3,4]})
X.eval("""B = A % 2 +1
D = #rowmax(A, B)
""", inplace = True)
I am interested in knowing other alternatives.
You should be able to call a custom or predefined function with #. In your case you want to call df.max(), so try this X.eval('#df.max()').
Hope this helps
this is a part of my matrix factorization code (a very weird version of nmf). My issue is that although every time when I iterate, I save the older copies of the W and H matrices, when I compare old_W and W after W finishes updating every time, they are actually the same! So the actual error output is always 0 and the while loop stops after the first iteration. However, "#print old - new" shows that the element W[r][i] is actually updated every time. What is it that I am not seeing?
def csmf(V, l, max_iter, err, alpha=0.01, beta=0.01, lamb=0.01):
W = np.random.rand(V.shape[0], l)
H = np.random.rand(l, V.shape[1])
n = V.shape[0]
N = V.shape[1]
NwOone = 60
NwOtwo = 60
NhOone = 50
NhOtwo = 50
for t in range(max_iter):
old_W = W # save old values
old_H = H
old = criterion(V,old_W,old_H,l,alpha,beta,lamb)
print "iteration ", t
##### update W
print "updating W"
setw = range(0,n)
subset_one = random.sample(setw,NwOone)
subset_two = calcGw(V, W, H, n, l, alpha, beta, NwOtwo)
chosen = np.intersect1d(subset_one,subset_two)
for r in chosen:
for i in range(len(W[0])):
update = wPosNeg(W[r],N,i,l,V,r,beta,H)
old = W[r][i]
W[r][i] = update
new = W[r][i]
#print old - new
##### update H
print "updating H"
seth = range(0,N)
subset_oneh = random.sample(seth,NhOone)
subset_twoh = calcGh(V, W, H, N, l, NhOtwo,lamb)
chosenh = np.intersect1d(subset_oneh,subset_twoh)
for s in chosenh: # column
for i in range(len(H)):
updateh = hPosNeg(H[i],n,i,l,V,s,lamb,W)
H[i][s] = updateh
##### check err
print "Checking criterion"
print criterion(V,W,H,l,alpha,beta,lamb)
print criterion(V,old_W,old_H,l,alpha,beta,lamb)
actual = abs(criterion(V,W,H,l,alpha,beta,lamb) -criterion(V,old_W,old_H,l,alpha,beta,lamb))
if actual <= err: return W, H, actual
return W, H, actual
dmat = np.random.rand(100,80)
W, H, err = csmf(dmat, 1, 10, 0.001, alpha=0.001, beta=0.001, lamb=0.001)
print err
in these lines:
old_W = W # save old values
old_H = H
you are not saving a copy, you are keeping a reference (old_W and W are the same piece of memory).
Try this:
old_W = W.copy() # save old values
old_H = H.copy()