我的代码从google驱动器下载.xlsx文件(使用pydrive),找到一些带有pandas的空白单元格,并使用openpyxl填充这些空白单元格。
当我打开openpyxl更改的文件时,一切看起来都很棒。但是,当我使用pandas read_excel函数时,所有具有方程的单元格都被读取为空白。我怀疑问题出在openpyxl上,因为当我在驱动器上预览文件时,这些单元格是空白的。 openpyxl没有触及的文件没有问题。
看起来我的问题与this one非常相似,但由于我的目标只是保持公式不变(我只想填空白单元格),我真的不想解析公式和我不确定如何或是否应用Felipe's修复。
我希望能够下载文件以使用散景进行绘制,而用户和python都将编辑程序,所以我真的很喜欢pandas能够读取方程式它是用户修改的文件或openpyxl修改过的文件。文件中的方程式是单击并拖动“共享方程式”,并且我希望尽可能保持这种方式,所以理想情况下我希望避免使用data_only=True
。我尝试指定data_only=False
,但这似乎没有改变任何内容。
我正在使用openpyxl 2.3.5 2.4,并且在代码运行时我保持excel关闭。
openpyxl修改前后的文件版本为available here。
我的代码在这里,所有openpyxl代码都被隔离到: #导入库 导入日期时间 进口进出口 进口口 将pandas导入为pd 来自openpyxl import load_workbook 来自itertools import islice #散景互动的相对导入
dl = imp.load_source('downloader', os.getcwd() +
'/Project/downloader.py')
gdu = imp.load_source('googledriveutils', os.getcwd() +
'/Project/googledriveutils.py')
remove_file = gdu.remove_file
find_folderid = gdu.find_folderid
get_file_list = gdu.get_file_list
# Define constants
COL_LABEL = '\nProbe - '
# TODO: ORP PROBE: REVISE THIS DATE when orp probe is added
IGNORE_BEFORE = pd.to_datetime('5.24.2016')
PROBE_DICT = {'DO (mg/L)': 'DO mg/L',
'pH': 'pH',
'NH4+ (mgN/L)': 'Ammonium',
'ORP (mV)': 'ORP mV'}
TS = '\nTimestamps'
def save_to_workbook(newval,
date,
header,
rows_to_skip=12,
wbname='temp.xlsx',
sheet_name='Reactor Data'):
wb = load_workbook(wbname)
ws = wb[sheet_name]
for cell in ws[rows_to_skip+1]:
# TODO: Error if header isn't found
if cell.value == header:
colno = cell.col_idx
break
for row in ws.iter_rows(min_row=rows_to_skip+1, min_col=1, max_col=1):
for cell in row:
# TODO: Error if date isn't found
if cell.value == date:
rowno = cell.row
break
ws.cell(row=rowno, column=colno).value = newval
wb.save(wbname)
return df
def find_r1masterfile():
# Navigate through the directories
wlab_fid = find_folderid('Winkler Lab', 'root')
kp_fid = find_folderid('KathrynsProjects', wlab_fid)
amxrct_fid = find_folderid('Anammox Reactor', kp_fid)
trials_fid = find_folderid('Reactor Trials', amxrct_fid)
# List files in directory
file_list = get_file_list(trials_fid)
for afile in file_list:
if afile['title'] == 'AMX RCT.xlsx':
# Return the file we asked for
return afile
# TODO: error if there was no file with that name
def save_r1masterfile(csv, rows_to_skip=12, filename='temp.xlsx', sheet_name='Reactor Data'):
# Get the file we want
master_file = find_r1masterfile()
try:
master_file.GetContentFile(filename)
except Exception, e:
print "Warning: Something wrong with file R1 Master File."
print str(e)
# TODO: add an email alarm to responsible user
if csv:
return master_file
else:
# convert to dataframe
wb = load_workbook(filename, data_only=True)
ws = wb[sheet_name]
print ws["B14"].value
data = ws.values
data = list(data)[rows_to_skip:]
cols = list(data[0])
del cols[0]
del data[0]
idx = [r[0] for r in data]
data = (islice(r, 1, None) for r in data)
df = pd.DataFrame(data, index=idx, columns=cols)
print df.dropna(how='all')
remove_file(filename)
return df
def upload_r1masterfile(filename='temp.xlsx'):
# Get the file we want
master_file = find_r1masterfile()
try:
master_file.SetContentFile(filename)
master_file.Upload()
except Exception, e:
print "Warning: Something wrong with file R1 Master File."
print str(e)
# TODO: add an email alarm to responsible user
def populate_r1masterfile(rows_to_skip=12, filename='temp.xlsx'):
# Get the R1 master file as a file
save_r1masterfile(True)
# Convert the juicy stuff to a dataframe
masterdf = pd.read_excel(filename,
sheetname='Reactor Data',
encoding="utf-16",
skiprows=rows_to_skip,
sep='\t',
index_col='Date',
keep_default_na=False,
na_values=['-1.#IND', '1.#QNAN', '1.#IND',
'-1.#QNAN', '','N/A', '#NA', 'NA'
'NULL', 'NaN', '-NaN', 'nan', '-nan'])
# Find what we will populate with probe data
# Find timestamps
ts_columns = [col for col in masterdf.columns if TS in col]
tsdf = masterdf[ts_columns]
# Find probes, ignore before given date
probe_columns = [col for col in masterdf.columns if COL_LABEL in col]
probedf = masterdf[probe_columns]
probedf = probedf[masterdf.index > IGNORE_BEFORE]
# Find Indices and column labels of blank values
stackdf = probedf.stack(dropna=False)
empty = stackdf[stackdf.isnull()].index.tolist()
# For each blank look for the probe, time & date of cycle, and return val
for each in empty:
probe, time = each[1].split(COL_LABEL)
time = tsdf.loc[each[0], time+TS]
ts = each[0]+pd.DateOffset(hour=time.hour, minute=time.minute)
val = dl.get_val_from(1, ts, PROBE_DICT.get(probe))
probedf.set_value(each[0], each[1], val)
# Save that value to the workbook
save_to_workbook(val, each[0], each[1])
upload_r1masterfile()
print 'Master file updated. ' + str(datetime.datetime.now())
remove_file('temp.xlsx')
return probedf
我根据查理的建议修改了我的代码(上面更新)。但我仍然在数据框中获得Nones。为了提供更具体的示例,为什么我在运行此代码时会这样做:
from openpyxl import load_workbook
wb = load_workbook('AMX RCT mod.xlsx', data_only=True)
ws = wb['Reactor Data']
print 'Value of B14 Formula is: ' + str(ws["B14"].value)
this file,我回来了?:
Value of B14 Formula is: None
有解决方法吗?
答案 0 :(得分:1)
使用openpyxl 2.4,您可以在一次通过中完成所需的操作。我已经完成了你的第一个功能并进行了调整。
from itertools import islice
from pandas import DataFrame
def save_to_workbook(newval,
date,
header,
rows_to_skip=12,
wbname='temp.xlsx',
sheet_name='Reactor Data'):
wb = load_workbook(wbname)
ws = wb[sheet_name]
rowno = None
colno = None
for cell in ws[1]:
# TODO: Error if header isn't found
if cell.value == header:
colno = col
for row in ws.iter_rows(min_row=rows_to_skip+1, min_col=1, max_col=1):
for cell in row:
# TODO: Error if date isn't found
if cell.value == date:
rowno = row
break
# TODO: Fix this
ws.cell(row=rowno, column=colno).value = newval
# convert to dataframe
data = ws.values
cols = next(data)[1:]
data = list(data)
idx = [r[0] for r in data]
data = (islice(r, 1, None) for r in data)
df = DataFrame(data, index=idx, columns=cols)
return df
这可能并不是你想做的一切,但希望能让你开始。它还避免了保存和解析整个工作簿,这可能会使它快得多。
要使用openpyxl 2.4,您需要执行pip install -U --pre openpyxl
或使用结帐。
有关一起使用openpyxl和pandas的详细信息,请参阅documentation。
答案 1 :(得分:0)
来自mailing list的查理答案:
因此,如果您想保留公式,则不得使用仅数据模式 如前所述,openpyxl永远不会评估公式,所以如果你想要 知道A3的值必须将文件传递给Excel等应用程序 或OpenOffice - 你可以运行OpenOffice无头或者这种东西 使用xlwings for Excel - 确实进行公式评估。然后你可以 以仅数据模式读取此文件以查看计算结果 或者你可以尝试使用像PyCel这样的东西来做 对你的评价。但是,基本上如果你想做计算:做它们 在Python中。
根据他的建议,我的解决方法是逐列重做所有计算,因为它们是在excel文件中完成的。 I.E.对于像这样的excel文件:
col1 col2 col3 col4
row1 1 3 =A1+B1 =1+3
row2 2 4 =A2+B2 =2+4
我将其导入为这样的数据帧(将方程式维护为字符串):
wb = load_workbook(filename, data_only=False)
ws = wb[sheet_name]
data = ws.values
cols = next(data)[1:]
data = list(data)
idx = [r[0] for r in data]
data = (islice(r, 1, None) for r in data)
df = DataFrame(data, index=idx, columns=cols)
然后执行以下操作:
parse_excel = lambda x: eval(str(x)[1:]) if isinstance(x, str) else x
for col in df.columns:
try:
df[col] = df[col].map(parse_excel)
except:
pass
df['col3'] = df['col2']+df['col1']
我确定这可能是最笨拙的方式,但它现在有效。