def load_master_initial_merge():
"""
GitHub Issue #3 should be fixed:
Initial merge is Master SCORP .csv + GEO .xlsx file.
Logic should pull street type and
address fields from Geo and overwrite master.
"""
print("Loading SCORP Master...")
scorp_master_file = r"State Comprehensive Outdoor Recreation Plan Inventory of Facilities\MasterSCORP_Base.xlsx"
print("Loading SCORP GEO...")
geo_master_file = r"State Comprehensive Outdoor Recreation Plan Inventory of Facilities\SCORP_FILTER_GEO.xlsx"
# State index_col so that data matches correctly.
sm = pd.read_excel(scorp_master_file, index_col="OBJECTID")
gm = pd.read_excel(geo_master_file, index_col="OBJECTID")
# Take Street type, street, town from geo, where available.
sm["Street_Type"] = gm["Street_Type"]
sm["Street"] = gm["Street"]
sm['Town'] = gm['Town']
print("Updated Street_Type, Street, Town from GEO to Master.")
# Export MasterSCORP_Updated.csv to be new master,
# then return the dataframe to whomeever called it.
export_filename = r"State Comprehensive Outdoor Recreation Plan Inventory of Facilities\MasterSCORP_Updated.csv"
print("Exporting to {}".format(export_filename))
sm.to_csv(export_filename)
return sm
def magicitems(dict, roll, row):
if dict[roll]['MI Numb'] != '0':
if dict[roll]['MI Numb 2'] != '0':
times2 = diceroller(dict[roll]['MI Numb 2'])
y = 0
items2dict = {}
try:
mdf2 = pd.read_excel('Items.xlsx', sheet_name = dict[roll]['Item 2'], index_col = 0, usecols = 'E:F')
mitems2 = dictcreator(mdf2)
while y < times2:
rolls = random.randint(1,100)
items2dict['var_' + str(y)] = tk.Label(root, text = mitems2[rolls]['Item']).grid(row = row + 1, column = 1, columnspan = 2)
y += 1
row += 1
except SyntaxError:
pass
times = diceroller(dict[roll]['MI Numb'])
x = 0
itemsdict = {}
try:
mdf = pd.read_excel('Items.xlsx', sheet_name = dict[roll]['Item'], index_col = 0, usecols = 'E:F')
mitems = dictcreator(mdf)
while x < times:
rolls = random.randint(1,100)
itemsdict["var_" + str(x)] = tk.Label(root, text = mitems[rolls]['Item']).grid(row = row + 1, column = 1, columnspan = 2)
itemsdict["var_" + str(x)] = tk.Label(root, text = mitems[rolls]['Item']).grid(row = row + 1, column = 1, columnspan = 2)
itemsdict["var_" + str(x)] = tk.Label(root, text = mitems[rolls]['Item']).grid(row = row + 1, column = 1, columnspan = 2)
x += 1
row += 1
except SyntaxError:
pass
art(dict, roll, row)
def main():
# Several datafiles, each with a long list of subjects
# Directory path variable assignment, assumes script is in working directory!!!
DATA = "data"
MEASURE = "measure"
EXCEL = "excel_files"
# Mainly for testing purposes
if len(sys.argv) > 1:
DATA = os.path.join(sys.argv[1], DATA)
MEASURE = os.path.join(sys.argv[1], MEASURE)
FINAL = os.path.join(sys.argv[1], FINAL)
# Create a dictionary with Subtest #s as the keys and a list of the data
# file as values. Uses a Dictionary Comprehension
SubTestIndex = [os.path.split(_file)[1].split('_')[0].split('Test')[1] for _file in glob(os.path.join(DATA,"*.txt"))]
for sID in SubTestIndex: # sID => subtest ID, eg. Sub[03A]
pXLXS = os.path.join(EXCEL, "Sub{0}_person_measure.xlsx".format(sID))
pTXT = os.path.join(MEASURE, "Sub{0}_person_measure.txt".format(sID))
if os.path.exists(pXLXS):
person_measure = pd.read_excel(pXLXS, header=None, names=['Scores', 'NaN', 'SubID', '_SubID', '_NaN'])
person_output = person_measure[['SubID', 'Scores']]
person_output.to_csv(pTXT, sep='\t', index=False, header=False)
iXLXS = os.path.join(EXCEL, "Sub{0}_item_measure.xlsx".format(sID))
iTXT = os.path.join(MEASURE, "Sub{0}_item_measure.txt".format(sID))
pd.read_excel(iXLXS, header=None).to_csv(iTXT, sep='\t', index=False, header=False)
def grouping_parcels(group): # Parcel to census lookup df = pd.read_csv(r'R:\Brice\gis\parcels_urbansim_census.txt') # add low income geography tag low_inc = pd.read_excel(r'R:\Brice\gis\special-needs\ACS_15_5YR_Low-income.xlsx', sheetname='Map-income') minority = pd.read_excel(r'R:\Brice\gis\special-needs\ACS_15_5YR_Minority.xlsx', sheetname='Mapping') if group == 'low_income': # Threshold for % of households as total for determining low income or not income_threshold = 0.5 # Define low_inc tracts as those with more HH below 200% median income than those above it low_inc['% low inc'] = low_inc['Below200']/low_inc['Total'] # Create flag for whether low income or not low_inc.ix[low_inc['% low inc'] >= income_threshold,'low_inc_tract'] = 1 low_inc.ix[low_inc['% low inc'] < income_threshold,'low_inc_tract'] = 0 # Merge with parcel file newdf = pd.merge(df, low_inc[['GEOID10','low_inc_tract']], on='GEOID10', how='left') parcels_list = newdf[newdf['low_inc_tract'] == 1].parcelid.values elif group == 'minority': minority['% minority'] = minority['Minority']/minority['Total'] minority_threshold = 0.5 minority.ix[minority['% minority'] >= minority_threshold, 'minority_tract'] = 1 minority.ix[minority['% minority'] < minority_threshold, 'minority_tract'] = 0 # Merge with parcel file newdf = pd.merge(df, minority[['GEOID10','minority_tract']], on='GEOID10', how='left') parcels_list = newdf[newdf['minority_tract'] == 1].parcelid.values return parcels_list
def preprocess_greyc_nislab(in_file, out_file):
"""
Preprocess the raw GREYC NISLAB dataset
"""
df = pd.concat([pd.read_excel(in_file, sheetname=0),
pd.read_excel(in_file, sheetname=1),
pd.read_excel(in_file, sheetname=2),
pd.read_excel(in_file, sheetname=3),
pd.read_excel(in_file, sheetname=4)])
df = df[df['Class'] == 2]
df['age'] = (df['Age'] < 30).map({True: '<30', False: '>=30'})
df['gender'] = df['Gender'].map({'F': 'female', 'M': 'male'})
df['handedness'] = df['Handedness'].map({'L': 'left', 'R': 'right'})
df['session'] = np.arange(len(df))
df['password'] = df['Password'].map({
'leonardo dicaprio': 1,
'the rolling stones': 2,
'michael schumacher': 3,
'red hot chilli peppers': 4,
'united states of america': 5,
})
def preprocess_row(idx_row):
idx, row = idx_row
keyname = list(map(lambda x: 'space' if x == ' ' else x, list(row['Password'])))
v = np.array(row['Keystroke Template Vector'].strip().split()).astype(int) // 10000
s = len(keyname) - 1
pp, rr, pr, rp = [v[s * i:s * (i + 1)] for i in range(4)]
timepress = np.r_[0, pp].cumsum()
# Offset the first release time by the duration of the first key
timerelease = np.r_[rp[0] - rr[0], rr].cumsum()
# There are ~180 rows where timerelease == timepress.
# Fix these by assuming at least the minimum standard clock resolution
timerelease[timerelease == timepress] += 16
sample = pd.DataFrame.from_items([
('user', row['User_ID']),
('session', row['session']),
('password', row['password']),
('age', row['age']),
('gender', row['gender']),
('handedness', row['handedness']),
('timepress', timepress),
('timerelease', timerelease),
('keyname', keyname)
])
return sample
df = pd.concat(map(preprocess_row, df.iterrows()))
df = df.set_index(['user', 'session'])[COLS]
df = remove_repeated_keys(df)
df.to_csv(out_file)
return
def labelspiezo(self) :
"""
Uses two sets of labels from EEG scorers to keep
only those segments corresponding to agreedupon
scores
"""
# First get file names
lbls1name = self.filename.strip('.mat') + '.xls'
lbls2name = self.filename.strip('.mat') + '_2.xls'
# Import scores as dataframes
lbls1 = pd.read_excel(self.filepath+lbls1name, header = None)
lbls2 = pd.read_excel(self.filepath+lbls2name, header = None)
# Concatenate into same dataframe and keep segments where equal
concatted = pd.concat([lbls1[0],lbls2[0]],1)
concatted.columns = ['scorer1','scorer2']
scoredf = concatted[concatted['scorer1']==concatted['scorer2']]
# scoredf is a dataframe with indices corresponding to the piezo
# segments where there is agreement, and the identical labels in
# each column
# first reshape the piezo
npr = np.reshape(self.piezo,[len(self.piezo)/(self.fs*4),self.fs*4])
# this single function slices the reshaped piezo matrix such that
# it retains only segments where doublescored
self.piezomat = npr[scoredf.index]
# as_matrix ensures indices are not saved since we need only labels
self.labels = scoredf['scorer1'].as_matrix()
def vadir_get_cnames_replace(df_list, df_to_use):
"""
This function determines the column differecnes between each
of the excel files passed in.
INPUT: list of excel files to import and the file with the
right column names to use to compare against
OUTPUT: dictionary of excel files as keys and list of unmatched
columns as values of the dictionary
"""
columns_to_use = []
other_columns = {}
unmatched_c = {}
for df in df_list:
if df == df_to_use:
df_import = pd.read_excel(df)
c_row = vadir_column_data_row(df_import)
columns_to_use = vadir_clean_cnames(df_import, c_row)
unmatched_c[df] = columns_to_use
else:
df_import = pd.read_excel(df)
c_row = vadir_column_data_row(df_import)
other_columns[df] = vadir_clean_cnames(df_import, c_row)
for df, columns in other_columns.items():
unmatched_c[df] = [c for c in columns if c not in columns_to_use]
return unmatched_c
def download_iter(self, file, save_path="."):
file_path = os.sep.join([save_path, file])
if not os.path.exists(save_path):
os.mkdir(save_path)
with open(file_path, "wb") as cache:
try:
self.retrbinary("RETR %s" % file, cache.write)
except:
yield "", "", pandas.DataFrame(),False
return
if not zipfile.is_zipfile(file_path):
ef = pandas.ExcelFile(file_path)
yield file, ef.sheet_names[0], pandas.read_excel(ef),False
return
with zipfile.ZipFile(file_path, "r") as zip:
xlss = []
sheet_name = ""
for name in zip.namelist():
ef = pandas.ExcelFile(zip.open(name))
sheet_name = ef.sheet_names[0]
xls = pandas.read_excel(ef)
yield name, sheet_name, xls,True
xlss.append(xls)
def qmflt(name="qfl.xlsx", Width=1, Color='k'):
if("csv"in name):QmFLtRaw = pd.read_csv(name)
elif("xlsx"in name):QmFLtRaw = pd.read_excel(name)
QmFLtRaw = pd.read_excel(name)
qmfltline(Width, Color)
Points = len(QmFLtRaw)
for i in range(Points):
q = QmFLtRaw.at[i, 'Qm']
f = QmFLtRaw.at[i, 'F']
l = QmFLtRaw.at[i, 'Lt']
Q = 100 * q / (q + f + l)
F = 100 * f / (q + f + l)
L = 100 * l / (q + f + l)
x = Q / 2 + (100 - Q) * L / (L + F)
y = Q / 2 * math.sqrt(3)
plotpoint(x, y, QmFLtRaw.at[i, 'Size'], QmFLtRaw.at[i, 'Color'], QmFLtRaw.at[i, 'Alpha'],
QmFLtRaw.at[i, 'Marker'])
plt.savefig("QmFLt-Plot.png", dpi=600)
plt.savefig("QmFLt-Plot.svg", dpi=600)
plt.show()
def main(args):
#Load the answer key
#Answer key must have the headings ['Problem', 'Your Answer', 'Answer Format']
answer_df = pd.read_excel(args.answer_key, sheet=0)
#Score
scores = {}
#Go through the individual sheets
for student_answer in glob.glob(os.path.join( args.assign_dir, '*xlsx') ):
print(student_answer)
student_df = pd.read_excel(student_answer, sheet=0)
#Check to make sure that the column headings are equal
if (student_df.columns != answer_df.columns).all():
print('ERROR with: %s' % student_answer)
else:
#Proceed with grading
equal = (answer_df['Your Answer'].str.lower() == student_df['Your Answer'].str.lower())
#Count all the false values
eqval = equal.value_counts()
#Pull the students name
path, fname = os.path.split(student_answer)
student_name = fname.split('_')[0]
#True grade set
scores[student_name] = eqval[eqval.index == True].values[0]
#Sort and print a csv
with open('exam_scores.csv', 'w') as wfile:
print('Student,Score', file=wfile)
for sname in sorted( list(scores.keys()) ):
print( '%s,%d' % (sname, scores[sname]), file=wfile)
def get_from_excel(data_path, extra_sheet=None):
'''
This opens a file dialog allowing ot select an excel file containing
the tracked data, and returns a :class:`CellCluster` object.
Paramteters
-----------
data_path: the path to the excelTM file
Returns
-------
cellcluster : a :class:`CellCluster` instance
the container class for the tracking
Notes
-----
The excel file should follow the structure of `excel_trajs_example.xlsx`
in the project's `data` directory
'''
### Read the data
trajs = pd.read_excel(data_path, 0)
trajs.t_stamp = trajs.t_stamp.astype(np.int)
trajs.label = trajs.label.astype(np.int)
trajs.set_index(['t_stamp', 'label'],
inplace=True)
### The Trajectories class is a subclass of
### pandas DataFrame
### Parsing excel files tends to add NaNs to the data
trajs = Trajectories(trajs.dropna().sortlevel())
metadata = pd.read_excel(data_path, 1)
metadata = {name: value for name, value
in zip(metadata['Name'], metadata['Value'])}
metadata['FileName'] = data_path
store_path = metadata['FileName']
if '.' in store_path[-6:]:
store_path = ''.join(store_path.split('.')[:-1]+['.h5'])
else:
store_path = store_path+'.h5'
store_path = os.path.join(
os.path.dirname(data_path), store_path)
### The ObjectsIO class
objectsio = ObjectsIO(metadata=metadata, store_path=store_path)
cellcluster = CellCluster(objectsio=objectsio)
cellcluster.trajs = trajs
cellcluster.oio['trajs'] = trajs
if extra_sheet is not None:
try:
extra = pd.read_excel(data_path, extra_sheet)
cellcluster.extra = extra
cellcluster.oio['extra'] = extra
except:
print('Extra data from sheet {} not found in the file {}'.format(extra_sheet, data_path))
return cellcluster
def extract_bloomberg_excel(str_bbDataFile, str_bbIndexFile,is_excel):
'''
블룸버그에서 받아온 엑셀파일을 dataframe 형식으로 변경하여 저장
:param str_bbDataFile: 실제 데이터 파일
:param str_bbIndexFile: 메타파일
'''
global df_bbData, df_bbDataCol
if(is_excel):
#데이터
df_bbData = pd.read_excel(str_bbDataFile,'Sheet1')
df_bbData = df_bbData.ix[5:,:] #제목행 및 날짜 없는행 제거
df_bbData = df_bbData.replace('#N/A N/A','') #엑셀에서 데이터 없는 셀에 들어간 문자열 제거
df_bbData = df_bbData.convert_objects(convert_numeric=True) #모든 컬럼은 숫자형식으로 변환
#리스트
df_bbIndex = pd.read_excel(str_bbIndexFile, 'index')
df_bbIndex.columns = ['no','idx','cat','rgn','rgn2','rmk','undf']
df_bbDataCol = df_bbIndex[df_bbIndex['no'].isin(df_bbData.columns)][['no','idx','rgn2']]
#csv로 저장
df_bbData.to_csv('../data/DailyEconomicData.csv',sep='\t',encoding='utf-8')
df_bbDataCol.to_csv('../data/index.csv',sep='\t',encoding='utf-8')
else:
df_bbData = pd.read_csv('../data/DailyEconomicData.csv',sep='\t',encoding='utf-8')
df_bbDataCol = pd.read_csv('../data/index.csv',sep='\t',encoding='utf-8')
def xl_to_df(directory, file_dict): # Get excel file file_path = '' for file in file_dict: if not file['type'] == 'questions': file_path = str(directory) + '\\' + file['name'] else: file_path2 = str(directory) + '\\' + file['name'] main1 = pd.read_excel(file_path, pd.ExcelFile(file_path).sheet_names[0], encoding='utf-8') main2 = pd.read_excel(file_path2, pd.ExcelFile(file_path2).sheet_names[0], encoding='utf-8') # xls_file = pd.ExcelFile(file_path) # main1 = xls_file.parse( xls_file.sheet_names[0] ) # xls_file2 = pd.ExcelFile(file_path2) # main2 = xls_file2.parse( xls_file2.sheet_names[0] ) full_df = main1.append(main2) return full_df
def get_context_data(self, **kwargs):
context = super(DocumentDetail, self).get_context_data(**kwargs)
filename = settings.MEDIA_ROOT+"/"+self.object.document.name
if self.object.doc_type == 1:
sniffer = csv.Sniffer()
dialect = sniffer.sniff(open(filename, 'r').read(), delimiters='\t,;') # defining the separator of the csv file
df = read_csv(filename, delimiter=dialect.delimiter)
context['input'] = df[:50].to_html()
else:
try:
df = read_excel(filename, sheetname="PMS")
context['PMS'] = df.to_html()
except:
pass
try:
df = read_excel(filename, sheetname="PMS1")
context['PMS1'] = df.to_html()
except:
pass
try:
df = read_excel(filename, sheetname="DS1")
context['DS1'] = df.to_html()
except:
pass
try:
df = read_excel(filename, sheetname="DS2")
context['DS2'] = df.to_html()
except:
pass
return context
def load_references(xls_filename, errors, validation_errors):
# Output columns can be different. Update according to the rename_columns dict:
try:
dfs = pandas.read_excel(xls_filename,
[#'core-24-depts',
'(reference) senior-staff-grades',
'(reference) professions',
'(reference) units',
])
except XLRDError, e:
if str(e) == "No sheet named <'(reference) units'>":
validation_errors.append(str(e))
return {}
elif str(e) in ("No sheet named <'(reference) senior-staff-grades'>",
"No sheet named <'(reference) professions'>"):
# this doesn't matter - we will use the standard_references
# anyway. Read it again, just for the units.
try:
dfs = pandas.read_excel(xls_filename, ['(reference) units'])
except XLRDError, e:
if str(e) == "No sheet named <'(reference) units'>":
validation_errors.append(str(e))
else:
errors.append(str(e))
return {}
def excel(FilePath, FileName, SheetNameOrNone, *args, **kwargs):
IndexColumn = kwargs.get('IndexColumn',None)
from pandas import read_excel
if FilePath.endswith('\\'):
lastslash=''
else:
lastslash='\\'
if FileName.endswith('.xlsx'):
fext=''
else:
fext='.xlsx'
while True:
try:
fullpath=FilePath+lastslash+FileName+fext
EmptyVar=read_excel(fullpath, SheetNameOrNone, index_col=IndexColumn, na_values=['NA'])
break
except:
fext='.xls'
fullpath=FilePath+lastslash+FileName+fext
EmptyVar=read_excel(fullpath, SheetNameOrNone, index_col=IndexColumn, na_values=['NA'])
break
return EmptyVar;
def get_hs300s():
"""
获取沪深300当前成份股及所占权重
Return
--------
DataFrame
code :股票代码
name :股票名称
date :日期
weight:权重
"""
try:
df = pd.read_excel(
ct.HS300_CLASSIFY_URL % (ct.P_TYPE["http"], ct.DOMAINS["idx"], ct.INDEX_C_COMM, ct.PAGES["hs300b"]),
parse_cols=[0, 1],
)
df.columns = ct.FOR_CLASSIFY_B_COLS
df["code"] = df["code"].map(lambda x: str(x).zfill(6))
wt = pd.read_excel(
ct.HS300_CLASSIFY_URL % (ct.P_TYPE["http"], ct.DOMAINS["idx"], ct.INDEX_C_COMM, ct.PAGES["hs300w"]),
parse_cols=[0, 3, 6],
)
wt.columns = ct.FOR_CLASSIFY_W_COLS
wt["code"] = wt["code"].map(lambda x: str(x).zfill(6))
return pd.merge(df, wt)
except Exception as er:
print(str(er))
def get_transfert_data_frames(year=None):
assert year is not None
default_config_files_directory = os.path.join(
pkg_resources.get_distribution("openfisca_france_indirect_taxation").location
)
matrice_passage_file_path = os.path.join(
default_config_files_directory,
"openfisca_france_indirect_taxation",
"assets",
"Matrice passage {}-COICOP.xls".format(year),
)
parametres_fiscalite_file_path = os.path.join(
default_config_files_directory,
"openfisca_france_indirect_taxation",
"assets",
"Parametres fiscalite indirecte.xls",
)
matrice_passage_data_frame = pandas.read_excel(matrice_passage_file_path)
if year == 2011:
matrice_passage_data_frame["poste2011"] = matrice_passage_data_frame["poste2011"].apply(
lambda x: int(x.replace("c", "").lstrip("0"))
)
parametres_fiscalite_data_frame = pandas.read_excel(parametres_fiscalite_file_path, sheetname="categoriefiscale")
selected_parametres_fiscalite_data_frame = parametres_fiscalite_data_frame[
parametres_fiscalite_data_frame.annee == year
]
return matrice_passage_data_frame, selected_parametres_fiscalite_data_frame
def __init__(self, file, year=None, level="Départements"):
"""
loads the data downloaded from `data.gouv.fr <http://www.data.gouv.fr/content/search?SortBy=Pertinence&SortOrder=0&SearchText=%C3%A9lections+2012>`_.
@param file xls file
@param year year (optional)
@param level ``Départements`` or ``Cantons``
"""
self.year = year
self.level = level.lower().replace("s", "")
if isinstance(file, list):
self.tours = file
else:
self.tours = [pandas.read_excel(file, sheetname="%s T1" % level),
pandas.read_excel(file, sheetname="%s T2" % level)]
for i, t in enumerate(self.tours):
if len(t) == 0:
raise Exception("no data for tour %d" % (i + 1))
self.tours = [self.process_tour(_) for _ in self.tours]
for i, t in enumerate(self.tours):
if len(t) == 0:
raise Exception("no data for tour %d" % i)
try:
self.tours = [
_.sort_values("Libellé du %s" % self.level, inplace=False) for _ in self.tours]
except Exception as e:
message = "unable to sort, shape={1} columns={0}".format(
",".join(self.tours[0].columns), self.tours[0].shape)
raise Exception(message) from e
def get_barres_seq_data(force=False):
global BARRES_SPECIES_DATA
if force or not os.path.exists(BARRES_SEQ_PATH):
LOGGER.info("Downloading Barres RNA Seq Data")
response = requests.get(BARRES_SEQ_URL, stream=True)
response.raise_for_status()
with open(BARRES_SEQ_PATH, mode="wb") as f:
for block in response.iter_content(1024):
f.write(block)
LOGGER.info("Reading Barres RNA Seq Data")
BARRES_SPECIES_DATA = {
"H**o sapiens": pd.read_excel(
BARRES_SEQ_PATH,
sheet_name="Human data only",
skiprows=[0],
).iloc[1:],
"Mus musculus": pd.read_excel(
BARRES_SEQ_PATH,
sheet_name="Mouse data only",
skiprows=[0],
),
}
def load_All_BAVs(BAVfile,sheet_names):
x = pandas.read_excel(BAVfile, sheet_names[0], index_col=0, na_values=['NA']).index
data= dict();
for sheet in sheet_names:
df= pandas.read_excel(BAVfile, sheet, index_col=0, na_values=['NA'])
x = intersect(x, df.index)
for sheet in sheet_names:
df =pandas.read_excel(BAVfile, sheet, index_col=0, na_values=['NA'])
good_cols = [col for col in df.columns if len(col.split("_"))==2]
df= df[good_cols]
df.columns = map(lambda x: x.split("_")[0],df.columns)
try:
del df[u"Tough"]
except:
print "oh well"
try:
del df[u"Visionary"]
except:
print "oh well"
df=df[pruned_words]
df = df.ix[x]
data[sheet]=df
return (x,data)
def test_excel_multindex_roundtrip(self, ext, c_idx_names, r_idx_names,
c_idx_levels, r_idx_levels):
# see gh-4679
with ensure_clean(ext) as pth:
if c_idx_levels == 1 and c_idx_names:
pytest.skip("Column index name cannot be "
"serialized unless it's a MultiIndex")
# Empty name case current read in as
# unnamed levels, not Nones.
check_names = r_idx_names or r_idx_levels <= 1
df = mkdf(5, 5, c_idx_names, r_idx_names,
c_idx_levels, r_idx_levels)
df.to_excel(pth)
act = pd.read_excel(pth, index_col=list(range(r_idx_levels)),
header=list(range(c_idx_levels)))
tm.assert_frame_equal(df, act, check_names=check_names)
df.iloc[0, :] = np.nan
df.to_excel(pth)
act = pd.read_excel(pth, index_col=list(range(r_idx_levels)),
header=list(range(c_idx_levels)))
tm.assert_frame_equal(df, act, check_names=check_names)
df.iloc[-1, :] = np.nan
df.to_excel(pth)
act = pd.read_excel(pth, index_col=list(range(r_idx_levels)),
header=list(range(c_idx_levels)))
tm.assert_frame_equal(df, act, check_names=check_names)
def test_excel_passes_na(self, read_ext):
excel = ExcelFile('test4' + read_ext)
parsed = pd.read_excel(excel, 'Sheet1', keep_default_na=False,
na_values=['apple'])
expected = DataFrame([['NA'], [1], ['NA'], [np.nan], ['rabbit']],
columns=['Test'])
tm.assert_frame_equal(parsed, expected)
parsed = pd.read_excel(excel, 'Sheet1', keep_default_na=True,
na_values=['apple'])
expected = DataFrame([[np.nan], [1], [np.nan], [np.nan], ['rabbit']],
columns=['Test'])
tm.assert_frame_equal(parsed, expected)
# 13967
excel = ExcelFile('test5' + read_ext)
parsed = pd.read_excel(excel, 'Sheet1', keep_default_na=False,
na_values=['apple'])
expected = DataFrame([['1.#QNAN'], [1], ['nan'], [np.nan], ['rabbit']],
columns=['Test'])
tm.assert_frame_equal(parsed, expected)
parsed = pd.read_excel(excel, 'Sheet1', keep_default_na=True,
na_values=['apple'])
expected = DataFrame([[np.nan], [1], [np.nan], [np.nan], ['rabbit']],
columns=['Test'])
tm.assert_frame_equal(parsed, expected)
def test_read_excel_nrows(self, read_ext):
# GH 16645
num_rows_to_pull = 5
actual = pd.read_excel('test1' + read_ext, nrows=num_rows_to_pull)
expected = pd.read_excel('test1' + read_ext)
expected = expected[:num_rows_to_pull]
tm.assert_frame_equal(actual, expected)
def test_read_excel_nrows_greater_than_nrows_in_file(self, read_ext):
# GH 16645
expected = pd.read_excel('test1' + read_ext)
num_records_in_file = len(expected)
num_rows_to_pull = num_records_in_file + 10
actual = pd.read_excel('test1' + read_ext, nrows=num_rows_to_pull)
tm.assert_frame_equal(actual, expected)
def test_reader_dtype(self, read_ext):
# GH 8212
basename = 'testdtype'
actual = pd.read_excel(basename + read_ext)
expected = DataFrame({
'a': [1, 2, 3, 4],
'b': [2.5, 3.5, 4.5, 5.5],
'c': [1, 2, 3, 4],
'd': [1.0, 2.0, np.nan, 4.0]}).reindex(
columns=['a', 'b', 'c', 'd'])
tm.assert_frame_equal(actual, expected)
actual = pd.read_excel(basename + read_ext,
dtype={'a': 'float64',
'b': 'float32',
'c': str})
expected['a'] = expected['a'].astype('float64')
expected['b'] = expected['b'].astype('float32')
expected['c'] = ['001', '002', '003', '004']
tm.assert_frame_equal(actual, expected)
with pytest.raises(ValueError):
pd.read_excel(basename + read_ext, dtype={'d': 'int64'})
def test_excel_read_buffer(self, read_ext):
pth = 'test1' + read_ext
expected = pd.read_excel(pth, 'Sheet1', index_col=0)
with open(pth, 'rb') as f:
actual = pd.read_excel(f, 'Sheet1', index_col=0)
tm.assert_frame_equal(expected, actual)
def __init__(self):
## PDZ Domains
temp_df = pd.read_excel(DATA+'\\theta_data.xlsx')
self.aminoacids = [acid.encode('utf-8') for acid in list(temp_df.columns[:20])]
self.df = temp_df.T
self.domains = [Domain(domain.encode('utf-8')) for domain in list(self.df.columns)]
self.domain_names = [domain.name for domain in self.domains]
### Peptide sequences
self.pep_seqs = []
self.pep_names = []
self.acid_names = ['Glycine', 'Alanine', 'Valine', 'Leucine', 'Isoleucine', 'Methionine', 'Proline', 'Phenylalanine', 'Tryptophan', 'Serine', \
'Threonine', 'Asparagine', 'Glutamine', 'Tyrosine', 'Cysteine', 'Lysine', 'Arginine', 'Histidine', 'Aspartate', 'Glutamate']
self.acid_dict = {self.aminoacids[i]:self.acid_names[i] for i in range(len(self.aminoacids))}
with open(DATA+'\\peptides.free') as f:
for line in f:
x = line.split()
self.pep_seqs.append(x[1])
self.pep_names.append(x[0])
self.peptides = [Peptide(name) for name in self.pep_names]
## Interaction: Which peptides bind to which domains
self.fp_interaction_matrix = pd.read_excel(DATA+"\\fp_interaction_matrix.xlsx")
for column in self.fp_interaction_matrix.columns:
self.fp_interaction_matrix.loc[self.fp_interaction_matrix[column] == 0.0, column] = -1.0
self.fp_interaction_matrix = self.fp_interaction_matrix.rename(columns = lambda x: str(x).replace(" ", ""))
## Classification matrix
self.class_matrix = np.zeros((2,2))
self.class_matrix[0,0] = 0.85
self.class_matrix[0,1] = 0.04
self.class_matrix[1,0] = 0.15
self.class_matrix[1,1] = 0.96
def __init__(self, db_filename = "fbo_solicitations.xlsx",
report_prefix = "report",
sol_sheet_name = "solicitations",
filtered_sheet_name = "filtered_solicitations",
index_column = "sponsor_number",
report_only_new = True):
'''
Constructor
'''
if(not os.path.isfile(db_filename)):
#generate a blank writable excel sheet from scratch
field_names = [field_name for field_name in Opportunity.fields]
field_names.remove("filtered")
writer = ExcelWriter(db_filename)
sol_df = pd.DataFrame(columns = field_names)
filtered_df = pd.DataFrame(columns = field_names)
sol_df.to_excel(writer,sol_sheet_name)
filtered_df.to_excel(writer,filtered_sheet_name)
writer.save()
writer.close()
self.report_filename = (report_prefix + "_"
+ str(datetime.today())[:19]
.replace(":","_").replace(" ","[") + "].xlsx")
#kept for posterity, in case only the date component is needed and we don't care about overwrites
#self.report_filename = report_prefix + "_" + str(date.today())
self.db_filename = db_filename
self.sol_sheet_name = sol_sheet_name
self.filtered_sheet_name = filtered_sheet_name
self.sol_df = pd.read_excel(db_filename,sol_sheet_name, index_col = index_column)
self.filtered_df = pd.read_excel(db_filename,filtered_sheet_name, index_col = index_column)
self.usaved_sol_counter = 0
self.sol_counter = 0
self.added_items = set()
def main(left_file="",
right_file="",
out_file="",
on=[],
how="left"):
"""given two xlsx (excel) workbooks, each containing one worksheet,
join the two worksheets and output a new workbook.
Parameters:
* `-l, --left-file`: The workbook which contains the worksheet
to consider the "left" table
* `-r, --right-file`: The workbook which contains the worksheet
to consider the "right" table
* `-o, --out-file`: The file to output the "joined" tables to
* `-O, --on`: A (space-seperated) list of column names to join on
* `-H, --how`: how to join the two tables, must be one of "left",
"right", "outer" or "inner"
For more information on joining tables please see the
[pandas dataframe merge documentation](http://pandas.pydata.org/pandas-docs/version/0.17.1/generated/pandas.DataFrame.merge.html)
"""
left = pd.read_excel(left_file)
right = pd.read_excel(right_file)
new = pd.merge(left, right, on=on, how=how)
print "SAVING {}".format(out_file)
new.to_excel(out_file, index=False)
df["grade"].cat.categories = ["very good", "good", "very bad"]
df["grade"] = df["grade"].cat.set_categories(["very bad", "bad", "medium",\
"good", "very good"])
print df["grade"]
print df.sort_values(by="grade")
print df.groupby("grade").size()
''' Plotting '''
ts = pd.Series(np.random.randn(1000),\
index=pd.date_range('1/1/2000', periods=1000))
ts = ts.cumsum()
ts.plot()
df = pd.DataFrame(np.random.randn(1000, 4), index=ts.index,\
columns=['A', 'B', 'C', 'D'])
df = df.cumsum()
plt.figure()
df.plot()
plt.legend(loc='best')
''' Getting Data In/Out '''
# CSV
df.to_csv('foo.csv')
print pd.read_csv('foo.csv')
# HDF5
df.to_hdf('foo.h5', 'df')
print pd.read_hdf('foo.h5', 'df')
# Excel
df.to_excel('foo.xlsx', sheet_name='Sheet1')
print pd.read_excel('foo.xlsx', 'Sheet1', index_col=None, na_values=['NA'])
''' Gotchas '''
os.chdir("/jukebox/wang/zahra/python/BrainPipe")
from tools.analysis.network_analysis import make_structure_objects
#set appropriate pth
src = "/jukebox/wang/zahra/kelly_cell_detection_analysis"
erode_pth = os.path.join(src, "annotation_allen_2017_25um_sagittal_erode_80um.tif")
dilate_pth = os.path.join(src, "dilated_atlases")
fig_dst = "/home/wanglab/Desktop"
df_pth = "/jukebox/LightSheetTransfer/atlas/allen_atlas/allen_id_table_w_voxel_counts_16bit.xlsx"
ann_pth = "/jukebox/LightSheetTransfer/atlas/allen_atlas/annotation_2017_25um_sagittal_forDVscans_16bit.tif"
#%%
#read vols
ann = sitk.GetArrayFromImage(sitk.ReadImage(ann_pth))
df = pd.read_excel(df_pth)
er_ann = tifffile.imread(erode_pth)
dl_anns = [os.path.join(dilate_pth, xx) for xx in os.listdir(dilate_pth)]
org_iids = np.unique(ann)[1:] #excluding 0
er_iids = np.unique(er_ann)[1:]
missing = [iid for iid in org_iids if iid not in er_iids]
missing_struct_names = [nm for nm in df.name.values if df.loc[df.name == nm, "id"].values[0] in missing] #excluding root
missing_struct_voxels = [df.loc[df.name == nm, "voxels_in_structure"].values[0] for nm in missing_struct_names]
#replace id column that matches to names
missing_struct_ids = [df.loc[df.name == nm, "id"].values[0] for nm in missing_struct_names]
#get parent names
missing_struct_parents = [df.loc[df["id"] == iid, "parent_name"].values[0]
# Import Built-Ins
import logging
# Import Third-Party
# Import Homebrew
import matplotlib.pyplot as plt
plt.style.use('bmh')
# Init Logging Facilities
log = logging.getLogger(__name__)
#################################################################
# 1- Load data
indicators_value = []
ticker_name = []
glob.glob("D:\Stock Study Excel Files\Input Excel Files\Stock USA\*.xlsx")
for f in glob.glob('D:\Stock Study Excel Files\Input Excel Files\Stock USA\*.xlsx'):
df = pd.read_excel(f)
# df.columns = map(str.capitalize, df.columns)
#df.rename(columns={'Volume': 'Volume_BTC'}, inplace=True)
tike = f.split('\\')[-1].split('.')[0]
print(tike)
df.insert(1, 'TICKER', tike) # to bring excel file name
# Clean nan values
df = ta.utils.dropna(df)
####################################################################
# 2-Add all ta features filling nans values (from Ta-Lib Except SuperTrend Because not in Ta-Lib)
df = ta.add_all_ta_features(df, "Open", "High", "Low", "Close", "Volume_BTC", fillna=True)
#####################################################################
# 3- Calculate
df['Signal'] = 0
sell = []
import matplotlib.pyplot as plt
import pandas as pd
url = 'https://s3.amazonaws.com/assets.datacamp.com/production/course_1606/datasets/winequality-red.csv'
df = pd.read_csv(url,sep=";")
print(df.head())
pd.DataFrame.hist(df.ix[:, 0:1])
plt.xlabel('fixed acidity (g(tartaric acid)/dm$^3$)')
plt.ylabel('count')
plt.show()
#Importing non-flat files from the web
import pandas as pd
url = 'http://s3.amazonaws.com/assets.datacamp.com/course/importing_data_into_r/latitude.xls'
xls = pd.read_excel(url,sheet_name=None)
print(xls.keys())
print(xls['1700'].head())
#Performing HTTP requests in Python using urllib
from urllib.request import urlopen,Request
url = "http://www.datacamp.com/teach/documentation"
request = Request(url)
response = urlopen(request)
print(type(response))
response.close()
#Printing HTTP request results in Python using urllib
from urllib.request import urlopen, Request
from pprint import pprint
import os
import json
import csv
from flask import (
Flask,
render_template,
jsonify,
request,
redirect,
url_for,)
dataset1 = pd.read_excel("BronxPropertySalesDatasets/sales_bronx_03.xls")
app = Flask (__name__)
SITE_ROOT = os.path.realpath(os.path.dirname(__file__))
json_url = os.path.join(SITE_ROOT, "data", "data.json")
data = json.load(open(json_url))
AIRBNB_SITE_ROOT = os.path.realpath(os.path.dirname(__file__))
airbnb_json_url = os.path.join(AIRBNB_SITE_ROOT, "data", "airbnb_data.json")
data_airbnb = json.load(open(airbnb_json_url))
PROPERTYSALES_SITE_ROOT = os.path.realpath(os.path.dirname(__file__))
def main():
import cdsapi
import numpy as np
import os
import pandas as pd
import math
def quarter_up(x):
return math.ceil(x * 4) / 4
def quarter_down(x):
return math.floor(x * 4) / 4
c = cdsapi.Client()
file = '/Volumes/Neely/BioDAR/ERA5/sites of light and suction traps.xlsx'
suction_traps = pd.read_excel(file, header=0, sheet_name='Suction traps')
number_of_traps = len(suction_traps['Lat'])
areas = []
trap_name = []
for a in range(0, number_of_traps):
lats = [
quarter_up(suction_traps['Lat'][a]),
quarter_down(suction_traps['Lat'][a])
]
longs = [
quarter_up(suction_traps['Long'][a]),
quarter_down(suction_traps['Long'][a])
]
areas.append([
max(lats),
min(longs),
min(lats),
max(longs),
])
trap_name.append(suction_traps['Trap name'][a].replace(" ", "_"))
start_year = 1979
stop_year = 2020
years = np.arange(start_year, stop_year + 1)
months = [
'01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12'
]
days = [
'01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12',
'13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24',
'25', '26', '27', '28', '29', '30', '31'
]
for year in years:
for month in months:
for day in days:
for idx, area in enumerate(areas):
try:
outdir = '/Volumes/Neely/BioDAR/ERA5/Myrna_TrapLocations_0_25_Box/suction_traps/pres_levels/' \
+ str(trap_name[idx]) + '/'
if not os.path.exists(outdir):
os.makedirs(outdir)
file_name = outdir + 'era5_pres_level_' + str(trap_name[idx]) + '_' + \
str(year) + str(month) + str(day) + '.nc'
print(str(trap_name[idx]), area)
print(file_name)
if os.path.isfile(file_name) == True:
print('exists')
continue
else:
c.retrieve(
'reanalysis-era5-pressure-levels', {
'product_type':
'reanalysis',
'format':
'netcdf',
'variable': [
'divergence',
'fraction_of_cloud_cover',
'geopotential',
'ozone_mass_mixing_ratio',
'potential_vorticity',
'relative_humidity',
'specific_cloud_ice_water_content',
'specific_cloud_liquid_water_content',
'specific_humidity',
'specific_rain_water_content',
'specific_snow_water_content',
'temperature',
'u_component_of_wind',
'v_component_of_wind',
'vertical_velocity',
'vorticity',
],
'pressure_level': [
'1',
'2',
'3',
'5',
'7',
'10',
'20',
'30',
'50',
'70',
'100',
'125',
'150',
'175',
'200',
'225',
'250',
'300',
'350',
'400',
'450',
'500',
'550',
'600',
'650',
'700',
'750',
'775',
'800',
'825',
'850',
'875',
'900',
'925',
'950',
'975',
'1000',
],
'year': [str(year)],
'month': [month],
'day': [day],
'time': [
'00:00',
'01:00',
'02:00',
'03:00',
'04:00',
'05:00',
'06:00',
'07:00',
'08:00',
'09:00',
'10:00',
'11:00',
'12:00',
'13:00',
'14:00',
'15:00',
'16:00',
'17:00',
'18:00',
'19:00',
'20:00',
'21:00',
'22:00',
'23:00',
],
'area':
area,
}, file_name)
except:
continue
def convert_xlsx(filename, sheetname, csv_name):
data_xls = pandas.read_excel(filename, sheetname, convert_float=False, index_col=None)
data_xls.to_csv(csv_name, encoding='utf-8')
def xl2csv(path):
df = pandas.read_excel(path)
csvfileloc = '/home/py01/Desktop/ratings5.csv'
df.to_csv(csvfileloc, sep='\t', encoding='utf-8', index=False)
os.remove(path)
items = [int(item) for item in items]
df[col] = pd.Series(items, dtype=int)
else:
df[col] = pd.Series(items, dtype=float)
##############################################################################
################################### MAIN #####################################
##############################################################################
if __name__ == '__main__':
# make output directory if not exists
cwd = os.getcwd()
if not cwd.endswith('/'):
cwd += '/'
if not OUTDIR.endswith('/'):
OUTDIR += '/'
try:
os.mkdir(OUTDIR)
print('Output folder created: %s' % (cwd + OUTDIR))
except:
pass
# iterate through each sheet and format / spit out CSV for D3
for i in range(len(SHEET_NAMES)):
print('\nWorking on %s sheet' % SHEET_NAMES[i])
df = pd.read_excel(INFILE, sheet_name=i+1, dtype=str, na_filter=False)
remove_trailing_whitespace(df)
typify_dataframe(df)
df.to_csv(OUTDIR + SHEET_NAMES[i] + '.csv', index=None)
print('Saved to %s' % (cwd + OUTDIR + SHEET_NAMES[i] + '.csv'))
import warnings
warnings.filterwarnings("ignore")
# Get dataset and features
#==============================#
aalist = list('ACDEFGHIKLMNPQRSTVWY')
def getAAC(seq):
aac = np.array([seq.count(x) for x in aalist])/len(seq)
return aac
data = pd.read_excel('sequence_ogt_topt.xlsx', index_col=0)
aac = np.array([getAAC(seq) for seq in data['sequence']])
ogt = data['ogt'].values.reshape((data.shape[0],1))
X = np.append(aac, ogt, axis=1)
sc = StandardScaler()
X = sc.fit_transform(X)
y = data['topt'].values
# Strategies and hyperparameters
#======================================#
# Hyperparameter range
cl_vals = [25.0, 30.0, None]
ch_vals = [72.2, 60.0]
import pandas as pd
from sklearn.neural_network import MLPClassifier
import numpy as np
import json
import scipy as sc
import numpy as np
train_file=pd.read_json(r'D:\Data Analysis\Project2\Project2\Data-Analysis-Project2\train.json', orient='records')
test_file=pd.read_csv(r'D:\Data Analysis\Project2\Project2\Data-Analysis-Project2\sample_solution.csv', header=0)
train_inp=pd.read_excel(r'D:\Data Analysis\Project2\Project2\Data-Analysis-Project2\Train_Input.xlsx',index_col=None,header=None)
test_inp=pd.read_excel(r'D:\Data Analysis\Project2\Project2\Data-Analysis-Project2\Test_Input.xlsx',index_col=None,header=None)
train_l=train_file['cuisine']
test_l=test_file['cuisine']
train_label=train_l[0:4000]
test_label=test_l[0:1000]
best=0
accuracy=0
hl=[1,3]
act=['logistic', 'tanh', 'relu']
sol=['lbfgs','sgd','adam']
al=[0.0001,0.0005]
bs=[64,128]
lr=['constant','invscaling','adaptive']
best_params = [0,0,0,0,0,0]
params = [0,0,0,0,0,0]
for h in hl:
for a in act:
import pandas as pd import sys input_file = sys.argv[1] output_file = sys.argv[2] data_frame = pd.read_excel(input_file, sheet_name='january_2013') writer = pd.ExcelWriter(output_file) data_frame.to_excel(writer, sheet_name='jan_13_output', index=False) writer.save()
category_compare, padding)
from BusinessPulseSurvey import business_pulse, qa_for_loc, qa_by_loc, compare_questions_locations, stacked_by_loc, qa_diff_by_loc
dir_path = os.path.dirname(os.path.abspath(__file__))
if not os.path.exists(config.log_dir):
os.makedirs(config.log_dir)
if not os.path.exists(config.log_dir + config.log_file):
with open(config.log_dir + config.log_file, 'w+'):
pass
logging.basicConfig(filename=config.log_dir + config.log_file,
level=logging.INFO)
logging.info('%s Economic Dashboard Started', datetime.datetime.now())
PUA_url = 'https://oui.doleta.gov/unemploy/docs/weekly_pandemic_claims.xlsx'
pua_data = pd.read_excel(PUA_url)
fileloc = config.fileloc
y2k = '2000-01-01'
cy = '2020-01-01'
rs = '2020-02-01' #Recession start date
#%% Overall Trends
def overall_trends():
logging.info('%s Overall Trends Started', datetime.datetime.now())
series = ['RSAFS', 'IPMAN', 'PAYEMS', 'DGORDER']
national_trends = fred_chart(series,
'2019-01-01',
transformation='index',
transform_date=rs,
def second(batch):
index_col_2_yr="B.TECH. II Yr.(III SEMESTER TIMETABLE) ODD SEMESTER 2018(Combined) JIIT128(Effective from 17/07/2018)"
data=pd.read_excel("timetable2.xlsx", index_col=index_col_2_yr)
#sperating cols
data.columns=[1,2,3,4,5,6,7,8,9]
data.columns.name=" "
#seprating days
mon=data.loc["MON":"TUE"].iloc[:-1]
tue=data.loc["TUE":"WED"].iloc[:-1]
wed=data.loc["WED":"THURS"].iloc[:-1]
thu=data.loc["THURS":"FRI"].iloc[:-1]
fri=data.loc["FRI":"SAT"].iloc[:-1]
sat=data.loc["SAT":].iloc[:-1]
#list of df
data2=[mon,tue,wed,thu,fri,sat]
final=data.dropna()
#data2
#final=data.dropna()
#final
#edit data frame here . make first row the column labels
rows=[]
#realgame
#move all to class and fns
for i in range (0,6):
newlist=[]
for j in range(1,10):
new=data2[i][j].dropna()
new2=new.str.contains(batch)
new3=new.str.contains('ALL')
new2=new2|new3
if not ((new[new2]).empty):
temp=new[new2].tolist()[0].replace("\n","")
#comment to show subject code
# temp1=temp.find('(')
# temp2=temp.find(')')
# temp=temp[:temp1]+temp[temp2+1:]
#comment to show subject code
temp3=temp.find('/')
temp=temp[:temp3]
newlist.append(temp)
#method 2
#newlist.append(new[new2].tolist().replace('\n',''))
else:
newlist.append(" ")
rows.append(newlist)
#final.append(new[new2])
#final
final=pd.DataFrame(rows,index=["Mon","Tue","Wed","Thu","Fri","Sat"],columns=data.iloc[0])
final.columns.name="Days/Time"
return(final.transpose().to_dict('list'))
# -*- coding: utf-8 -*-
# tf_idf1
#
# test script for setup of tf-idf
#
import os
import pandas as pd
from nltk.corpus import stopwords
from sklearn.feature_extraction.text import TfidfVectorizer
#add a project name for output
project_name = 'Daily Mail Property Articles'
# read sf export
sf_export = pd.read_excel(r'C:\Users\JLee35\Automation\TF-IDF\input\body_copy.xlsx')
corpus =[]
for i in sf_export.index:
doc = sf_export['Body Copy Full'][i]
corpus.append(doc)
# counts the length of the list
doc_count = len(corpus)
print(f'Total number of documents = {doc_count}')
# use TfidfVectorizer from Scikit-Learn to transform the corpus
stop = stopwords.words('english')
vectorizer = TfidfVectorizer(max_df=.65, min_df=1, ngram_range=(1,1), stop_words=stop, use_idf=True, norm=None)
transformed_documents = vectorizer.fit_transform(corpus)
transformed_documents_as_array = transformed_documents.toarray()
import numpy as np
import pandas as pd
import streamlit as st
import altair as alt
df=pd.read_excel('Base_sondage_maraichage.xlsx', index_col="Identifiant", na_values=['NA'])
df = df.fillna({"Mode_irrigation": "Pluvial"})
cleanup_nums = {
"Mode_Production": {"Principale": 1, "En succession": 2,
"En association": 3, "Sous étage": 4},
"Mode_irrigation": {"Localisée": 1, "Gravitaire": 2, "Aspersion": 3,
"Pivot": 4,
"Gravitaire,Localisée": 5, "Localisée,Pivot": 6, "Pluvial":7},
"Culture": {"Courgette": 1, "Pomme de terre": 2, "Tomate": 3,
"Coriandre et persil": 4,
"Haricot vert": 5, "Concombre": 6,
"Menthe": 7, "Fève vert": 8, "Aubergine": 9,
"Carotte": 10,
"Chou fleur": 11, "Oignon":12, "Choux vert":13, "Celeri": 14,
"Laitue": 15, "Tomate kiwat": 16, "Fraise": 17,
"Piment fort": 18,
"Artichaut": 19, "Absinthe": 20,
"Haricot Helda": 21, "Topinambour": 22, "Myrtille": 23,
"Endive": 24,
"Navet": 25, "Pastèque":26, "Poivron": 27},
"""
参考：
http://pbpython.com/market-basket-analysis.html
"""
import pandas as pd
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
def encode_units(x):
if x <= 0:
return 0
if x >= 1:
return 1
df = pd.read_excel('http://archive.ics.uci.edu/ml/machine-learning-databases/00352/Online%20Retail.xlsx')
print(df.head())
df['Description'] = df['Description'].str.strip()
df.dropna(axis=0, subset=['InvoiceNo'], inplace=True)
df['InvoiceNo'] = df['InvoiceNo'].astype('str')
df = df[~df['InvoiceNo'].str.contains('C')]
basket = (df[df['Country'] =="France"]
.groupby(['InvoiceNo', 'Description'])['Quantity']
.sum().unstack().reset_index().fillna(0)
.set_index('InvoiceNo'))
basket_sets = basket.applymap(encode_units)
basket_sets.drop('POSTAGE', inplace=True, axis=1)
#Importación de librerías
import plotly.offline as pyo
import plotly.graph_objs as go
import pandas as pd
#Carga de datos
df_temp = pd.read_excel(
r'C:\Users\ivan_pinar\Dropbox\Creación de MOCs\MOC Dash Python\Datasets\3.8\Temperaturas.xlsx'
)
#Definición de objeto de tipo lista "data", x --> Categorízación, y --> Valores a verificar distribución
data = [
go.Box(x=df_temp["Ciudad"], y=df_temp["T_Promedio"])
] #pointpos=0 para ubicación de los puntos en el centro / boxpoints ="all" si se quieren visualizar todos los puntos
#Definición de objeto "layout": diseño del gráfico como título, nombres de ejes,...
layout = go.Layout(title="Box & whiskers Temperatura")
#Creación de objeto "Figure" de Plotly a partir de los objetos data y layout creados previamente
fig = go.Figure(data=data, layout=layout)
#Generación del plot a partir de la figura definida y nombre del fichero de salida HTML
pyo.plot(fig, filename="3.8 Temp_Box Plot.html")
print('************************************************** Analyzing {:s}'.
format(state.upper()))
print()
#excel = r'K:\DEEP_SOLAR_BIG2\TN\TNDeep_Solar.xlsx'
if region == TVA_l:
#excel = r'K:\TVA_SVI\TVA_DS_SVI_merged.xlsx'
excel = r'K:\TVA_SVI\TVA_DS_SVI_merged2.xlsx'
elif region == TVA_f:
#excel = r'K:\TVA_SVI\TVA_DS_SVI_merged.xlsx'
excel = TVA_path
else:
excel = r'K:\TVA_SVI\TN_DS_SVI_merged.xlsx'
#TNDS = pd.read_excel(excel, index='fips').fillna(0)
#TNDS = pd.read_excel(excel, index='fips').dropna(axis=0)
TNDS = pd.read_excel(excel, index='fips')
#TNDS = pd.read_excel(excel, index='fips')
#TNDS = TNDS.fillna(TNDS.mean(axis=1))
if region != TVA_l and region != TVA_f:
print('Getting region {:s}'.format(region))
TNDS = TNDS.loc[TNDS['ST_ABBR'] == region.upper()]
print('splitting data')
adopters, high, mod, non = split_data_res_adopt_non(TNDS,
hthr=10,
midrange=[1, 10],
lthr=1,
verbose=False)
dd.display_percentages(TNDS.shape[0],
adopters.shape[0],
def disp1():
import io
from pdfminer.converter import TextConverter
from pdfminer.pdfinterp import PDFPageInterpreter
from pdfminer.pdfinterp import PDFResourceManager
from pdfminer.layout import LAParams
from pdfminer.pdfpage import PDFPage
text = ''
def extract_text_from_pdf(pdf_path):
with open(pdf_path, 'rb') as fh:
# iterate over all pages of PDF document
for page in PDFPage.get_pages(fh,
caching=True,
check_extractable=True):
# creating a resoure manager
resource_manager = PDFResourceManager()
# create a file handle
fake_file_handle = io.StringIO()
# creating a text converter object
converter = TextConverter(resource_manager,
fake_file_handle,
codec='utf-8',
laparams=LAParams())
# creating a page interpreter
page_interpreter = PDFPageInterpreter(resource_manager,
converter)
# process current page
page_interpreter.process_page(page)
# extract text
text = fake_file_handle.getvalue()
yield text
# close open handles
converter.close()
fake_file_handle.close()
# calling above function and extracting text
#print(fname)
file_path = "D:/resume_analysis/static/resumes/" + fname
fp = file_path.split('/')
f = fp[len(fp) - 1]
for page in extract_text_from_pdf(file_path):
text += ' ' + page
#print(text)
import spacy
from spacy.matcher import Matcher
# load pre-trained model
nlp = spacy.load('en_core_web_sm')
# initialize matcher with a vocab
matcher = Matcher(nlp.vocab)
def extract_name(resume_text):
nlp_text = nlp(resume_text)
# First name and Last name are always Proper Nouns
pattern = [{'POS': 'PROPN'}, {'POS': 'PROPN'}]
matcher.add('NAME', None, pattern)
matches = matcher(nlp_text)
for match_id, start, end in matches:
span = nlp_text[start:end]
return span.text
name = extract_name(text)
#print(name)
import re
def extract_mobile_number(text):
phone = re.findall(
re.compile(
r'(?:(?:\+?([1-9]|[0-9][0-9]|[0-9][0-9][0-9])\s*(?:[.-]\s*)?)?(?:\(\s*([2-9]1[02-9]|[2-9][02-8]1|[2-9][02-8][02-9])\s*\)|([0-9][1-9]|[0-9]1[02-9]|[2-9][02-8]1|[2-9][02-8][02-9]))\s*(?:[.-]\s*)?)?([2-9]1[02-9]|[2-9][02-9]1|[2-9][02-9]{2})\s*(?:[.-]\s*)?([0-9]{4})(?:\s*(?:#|x\.?|ext\.?|extension)\s*(\d+))?'
), text)
if phone:
number = ''.join(phone[0])
if len(number) > 10:
return '+' + number
else:
return number
num = extract_mobile_number(text)
#print(num)
import re
def extract_email(email):
email = re.findall("([^@|\s]+@[^@]+\.[^@|\s]+)", email)
if email:
try:
return email[0].split()[0].strip(';')
except IndexError:
return None
email = extract_email(text)
#print(email)
import pandas as pd
import spacy
#from spacy.en import English
# load pre-trained model
nlp = spacy.load('en_core_web_sm')
#noun_chunk = nlp.noun_chunks
#nlp=English()
doc = nlp(text)
def extract_skills(resume_text):
nlp_text = nlp(resume_text)
# removing stop words and implementing word tokenization
tokens = [token.text for token in nlp_text if not token.is_stop]
#print(tokens)
# reading the csv file
data = pd.read_csv("D:/resume_analysis/techskill.csv")
# extract values
skills = list(data.columns.values)
skillset = []
# check for one-grams (example: python)
for token in tokens:
if token.lower() in skills:
skillset.append(token)
# check for bi-grams and tri-grams (example: machine learning)
for token in doc.noun_chunks:
token = token.text.lower().strip()
if token in skills:
skillset.append(token)
return [i.capitalize() for i in set([i.lower() for i in skillset])]
text = text.lower()
skill = extract_skills(text)
print(skill)
skill_len = len(skill)
excel_file = 'D:/resume_analysis/jd.xls'
jd = pd.read_excel(excel_file)
skill1 = jd['Skills']
row = jd.shape[0]
res = []
for i in range(row):
count = 0
sk = skill1[i].split(',')
for j in skill:
if (skill1[i].find(j) != -1):
count = count + 1
res.append(100 * count / len(skill))
ind = res.index(max(res))
print(jd['JobTitle'][ind])
res1 = []
for i in res:
res1.append(i)
res1 = sorted(res1)
p1 = max(res)
print(res1)
second = res1[len(res1) - 1]
third = res1[len(res1) - 2]
print(max(res), second, third)
res[ind] = -res[ind]
ind1 = res.index(second)
res[ind1] = -res[ind1]
ind2 = res.index(third)
print(ind1)
s1 = jd['Skills'][ind]
s2 = jd['Skills'][ind1]
s3 = jd['Skills'][ind2]
rs1, rs2, rs3 = [], [], []
for j in skill:
if (j in s1):
rs1.append(j)
for j in skill:
if (j in s2):
rs2.append(j)
for j in skill:
if (j in s3):
rs3.append(j)
return render_template('car.html',
job1=jd['JobTitle'][ind],
skills1=rs1,
job2=jd['JobTitle'][ind1],
skills2=rs2,
job3=jd['JobTitle'][ind2],
skills3=rs3,
p1=round(p1, 3),
fnam=f,
p2=round(second, 3),
p3=round(third, 3))
def read_file(path,name):
df = pd.read_excel('%s/%s'%(path,name))
return df
driver.find_element_by_name('password').send_keys('t4')
time.sleep(.2)
driver.find_element_by_name('password').send_keys('78')
time.sleep(.2)
driver.find_element_by_name('password').send_keys('@g')
time.sleep(.2)
driver.find_element_by_name('password').send_keys('ma')
time.sleep(.2)
driver.find_element_by_name('password').send_keys('il')
time.sleep(.2)
driver.find_element_by_name('password').send_keys('.c')
time.sleep(.2)
driver.find_element_by_name('password').send_keys('om')
driver.find_element_by_name('password').send_keys(Keys.RETURN)
time.sleep(60)
abc = pd.read_excel('C:\\Users\\acer\\Downloads\\cds\\mat.xls', header=None, index_col=False)
f = open('data.csv', 'a')
var=0
continueCheck = False
for item in abc.index:
print(abc[0][item])
website = str(abc[0][item])
if website == '6annonce.com':
continueCheck = True
continue
if continueCheck:
driver.get('https://pro.similarweb.com/#/website/worldwide-overview/'+website+'/*/999/3m?webSource=Total')
# try:
if driver.title != 'Pardon Our Interruption':
wait = WebDriverWait(driver, 40)
def createRUID_List(rowIdxList, headerStr):
"""
Loops over a series containing row indices and returns a list of RUID strings.
Inputs:
rowIdxList - collection of row index values
headerStr - DataFrame header string value for column containing RUIDs
Outputs:
new list containing RUID strings
"""
RUID_List = []
for aRowIdx in rowIdxList:
workingRUID=df[headerStr].iloc[aRowIdx]
RUID_List.append(workingRUID)
return RUID_List
df = pd.read_excel("abcd_rucdr_master_forPython.xlsx")
print ('Finished reading in input file.')
#blackList=['NDAR_INV']
#for pattern in blackList:
# df['pGUID_Rutgers'] = df['pGUID_Rutgers'].replace(pattern, '')
#datasets
Unique_DAIC_Invs = df['InvCodeDAIC_OnlyTxt'].dropna()
Unique_Rutgers_Invs = df['InvCodeRUCDR_OnlyTxt'].dropna()
AllRutgersInvs = df['InvCodeMinusDOTxt'].dropna()
AllDAIC_Invs = df['InvCodeMinusROTxt'].dropna()
print ('About to start first match2collections.')
def main():
df = pd.read_excel(INPUT_FILE)
wb = op.Workbook()
ws = wb.active
ws.append(['Месяц'] + ['Параметр'] + [i for i in range(1, 32)])
wells = list(dict.fromkeys(df.well))
# Iterate over well
for well in wells:
print(well)
ws.append([well])
last_row = ws.max_row
ws.merge_cells(start_row=last_row,
start_column=1,
end_row=last_row,
end_column=33)
well_df = df[df.well == well]
years = list(dict.fromkeys(well_df.date.dt.year))
# Iterate over year
for year in years:
year_df = well_df[well_df.date.dt.year == year]
months = list(dict.fromkeys(year_df.date.dt.month))
for month in months:
month_df = year_df[year_df.date.dt.month == month]
# Blank rows
q = [None for i in range(31)]
dynamic = [None for i in range(31)]
static = [None for i in range(31)]
# Iterate over month data
for _, row in month_df.iterrows():
day = row.date.day
q[day - 1] = round(row.rate, 1)
dynamic[day - 1] = round(row.dynamic, 1)
static[day - 1] = round(row.static, 1)
# Write rows to sheet
ws.append([f'{months_names[month - 1]} {year}'] +
['Q, м3/сут'] + q)
ws.append([None] + ['Нд, м'] + dynamic)
ws.append([None] + ['Нст, м'] + static)
last_row = ws.max_row
ws.merge_cells(start_row=last_row - 2,
start_column=1,
end_row=last_row,
end_column=1)
# Apply styles
for row in ws.iter_rows():
for cell in row:
cell.style = style_basic
if row[0].value is not None and row[1].value is None:
row[0].style = style_bold
for i in range(2, 33):
ws.column_dimensions[get_column_letter(i + 1)].width = 6
wb.save(OUTPUT_FILE)
def on_button_clicked(b):
clear_output()
display(button)
## UPLOADED INITIAL DATA
# if mv.value !='':
# try:
data = pd.read_excel('story_'+ story.value+'/story'+ story.value+'.xlsx', sheet_name='sample')
# data=data.drop(['FC_D','FC_E','FC_F'],axis=1)
data['Departure_Date']=pd.to_datetime(data.Departure_Date)
datadone=data[data.Departure_Date< pd.Timestamp(nowaday.value)]
data=data[data.Departure_Date>= pd.Timestamp(nowaday.value)]
## 1. Sort by Arival Date and Priority
data=data.sort_values(by=['Arrival_Date','Price'], ascending=[True,False])
## 2. Set Parameter and Constraint
#Total Floating Crane
a=1
b=1
c=1
totfc = int(fcnumber.value)
fclist=['FC_A','FC_B','FC_C']
#### Create feature demanddays for 1 floating crane
data['demanddays']= np.round(data.Demand_Qty/data.Loading_Rate)
data['demandfc']=np.ceil(data['demanddays']/data.Laytime_Duration)
data['demanddays_new']=np.ceil(data.Demand_Qty/(data.Loading_Rate*data['demandfc']))
## 3. Assign Floating Crane - Initial Plan
# to get initial plan
### create initial first row
import itertools
a=[]
for L in range(1, len(fclist)+1):
for subset in itertools.combinations(fclist, L):
# print(subset)
x=list(subset)
a.append(x)
a=[[1,0,0],
[0,1,0],
[0,0,1],
[1,1,0],
[1,0,1],
[0,1,1],
]
a=pd.DataFrame(a,columns=['FC_A', 'FC_B', 'FC_C'])
if (data.loc[0,'demandfc']==1) == True:
data.loc[0, 'FC_A'] = 1
data.loc[0, 'FC_B'] = 0
data.loc[0, 'FC_C'] = 0
elif (data.loc[0,'demandfc']==2) == True :
data.loc[0, 'FC_A'] = 1
data.loc[0, 'FC_B'] = 1
data.loc[0, 'FC_C'] = 0
else:
data.loc[0, 'FC_A'] = 1
data.loc[0, 'FC_B'] = 1
data.loc[0, 'FC_C'] = 1
### complete initial plan
for i in range(1,data.shape[0]):
if (data.loc[i,'demandfc'] == 1):
data.loc[i, 'FC_A'] = 1
data.loc[i, 'FC_B'] = 0
data.loc[i, 'FC_C'] = 0
elif (data.loc[i,'demandfc'] == 2) :
for fc in range(a.shape[0]):
if ((data.loc[i-1,'FC_A'])== (a.loc[fc,'FC_A'])) & ((data.loc[i-1,'FC_B'])== (a.loc[fc,'FC_B'])) & ((data.loc[i-1,'FC_C'])== (a.loc[fc,'FC_C'])):
data.loc[i, 'FC_A'] = np.abs((a.loc[fc,'FC_A'])-1)
data.loc[i, 'FC_B'] = np.abs((a.loc[fc,'FC_B'])-1)
data.loc[i, 'FC_C'] = np.abs((a.loc[fc,'FC_C'])-1)
if ((data.loc[i, 'FC_A'] + data.loc[i, 'FC_B'] +data.loc[i, 'FC_C'] )==1) & (data.loc[i, 'FC_A']==0):
data.loc[i, 'FC_A']=1
elif ((data.loc[i, 'FC_A'] + data.loc[i, 'FC_B'] +data.loc[i, 'FC_C'] )==1) & (data.loc[i, 'FC_B']==0):
data.loc[i, 'FC_B']=1
elif ((data.loc[i, 'FC_A'] + data.loc[i, 'FC_B'] +data.loc[i, 'FC_C'] )==1) & (data.loc[i, 'FC_C']==0):
data.loc[i, 'FC_C']=1
else:continue
else:continue
else:
data.loc[i, 'FC_A'] = 1
data.loc[i, 'FC_B'] = 1
data.loc[i, 'FC_C'] = 1
## 4. Recalculate Departure Date
# based on real demanddays_new
data['Arrival_Date_change']=pd.to_datetime(np.nan)
data['Departure_Date_change']=pd.to_datetime(np.nan)
data.loc['FC_gap_Date_change'] = pd.to_datetime(np.nan)
data=data[['MV', 'ETA','Arrival_Date', 'Laytime_Duration', 'Departure_Date',
'Demand_Qty', 'Loading_Rate', 'Price', 'Demurrage_Rate', 'demanddays',
'demandfc', 'demanddays_new',
'FC_A', 'FC_B', 'FC_C',
'FC_D','FC_E','FC_F',
'Arrival_Date_change', 'Departure_Date_change']]
datachange=pd.DataFrame([[mv.value,arvl.value]],columns=['MV','Arrival_Date_change_source'])
datachange['Arrival_Date_change_source']=pd.to_datetime(datachange.Arrival_Date_change_source)
data=pd.merge(data,datachange,how='left',on=['MV'])
data['Arrival_Date']=pd.to_datetime(data.Arrival_Date)
data['Departure_Date']=pd.to_datetime(data.Departure_Date)
# data['Arrival_Date_change']=pd.to_datetime(data.Arrival_Date_change)
data['Arrival_Date_change']=data['Arrival_Date_change_source']
data['Est_Departure_Date_change']=data['Arrival_Date_change']+pd.to_timedelta(data['demanddays_new'], unit='D')
data['Departure_Date_change']=data['Arrival_Date_change']+pd.to_timedelta(10, unit='D')
# data['Departure_Date_change']=pd.to_datetime(data.Departure_Date_change)
data.loc[data.Arrival_Date_change.isnull() ,'Arrival_Date_change']=data.loc[data.Arrival_Date_change.isnull() ,'Arrival_Date']
data['Est_Departure_Date_change']=data['Arrival_Date_change']+pd.to_timedelta(data['demanddays_new'], unit='D')# data.loc[0, 'Arrival_Date_change']=pd.to_datetime(data.loc[0,'Arrival_Date_change_source'])
# data.loc[0,'Departure_Date_change']=data.loc[0,'Arrival_Date_change']+pd.to_timedelta(data.loc[0,'demanddays_new'], unit='D')
data['Departure_Date_change']=data['Arrival_Date_change']+pd.to_timedelta(10, unit='D')
data.drop('Arrival_Date_change_source',axis=1,inplace=True)
x=datachange['MV'][0]
### 6. Check the next sequence Schedule
# If the departure date change is clash, so FC_start_date_change must be adjusted and check the potential demorage cost
## Sort by Arival Date Change and Priority (Price)
data=data.sort_values(by=['Arrival_Date_change','Price'], ascending=[True,False])
data=data.reset_index()
data.drop('index',axis=1,inplace=True)
data['FC_Start_Date_change']=data['Arrival_Date_change']
data['FC_End_Date_change']=data['Est_Departure_Date_change']
# data.loc[data.MV== x ,'FC_Start_Date_change']=data.loc[data.MV== x ,'Arrival_Date_change']
# data.loc[data.MV== x ,'FC_End_Date_change']=data.loc[data.MV== x ,'Est_Departure_Date_change']
# data.loc[(data.MV!= x) & (data.FC_Start_Date_change.isnull()),'FC_Start_Date_change']=data.loc[data.MV!= x ,'Arrival_Date_change']
# data.loc[(data.MV!= x) & (data.FC_End_Date_change.isnull()),'FC_End_Date_change']=data.loc[data.MV!= x ,'Est_Departure_Date_change']
# Calculate Demurage cost
data.loc[0,'Demmurage_Day']=0
data.loc[0,'Demmurage_Cost']=0
### Create Demmuragecost Simulation Function
def sim_demuragecost(totfc,data):
totfc=totfc
for i in range(1,data.shape[0]):
#if previous iteration row value is greater than current iteration row value then
if (data.loc[i-1,'Est_Departure_Date_change'] >= data.loc[i,'Arrival_Date_change']) :
totfc=totfc-data.loc[i-1,'demanddays_new']
#if available fc >= demand fc i
if (totfc >= data.loc[i,'demanddays_new'] ):
data.loc[i,'FC_Start_Date_change'] = data.loc[i,'Arrival_Date_change']
data.loc[i,'FC_End_Date_change'] = data.loc[i,'Est_Departure_Date_change']
# Calculate Demurage cost
data.loc[i,'Demmurage_Day']=np.ceil((data.loc[i,'FC_End_Date_change'] - data.loc[i,'Departure_Date_change'])/np.timedelta64(1,'D'))
data.loc[i,'Demmurage_Cost']=data.loc[i,'Demurrage_Rate'] * data.loc[i,'Demmurage_Day']
# data.loc[i,'FC_gap_Date_change'] = data.loc[i,'Departure_Date_change'] + pd.to_timedelta(1, unit='D')
# data.loc[i,'Demmurage_Day']=0 #will be no risk to get demurage day n cost
# data.loc[i,'Demmurage_Cost']=0
#if available fc < demand fc i and fc is at least available for one then
elif (totfc < data.loc[i,'demanddays_new']) & (totfc >0):
#state the available FC to start operate
data.loc[i,'FC_Start_Date_change'] = data.loc[i,'Arrival_Date_change']
data.loc[i,'FC_Start_Date_change_2'] = data.loc[i-1,'FC_End_Date_change'] + pd.to_timedelta(1, unit='D') #startdate next fc
#cal the number of days that available FC can start
data.loc[i,'dayrun_progress']=np.ceil((data.loc[i,'FC_Start_Date_change_2'] - data.loc[i,'FC_Start_Date_change'])/np.timedelta64(1,'D'))
#cal the remaining quantity that is already loaded by available FC
data.loc[i,'Demand_Qty_remain']= data.loc[i,'Demand_Qty'] - (data.loc[i,'Loading_Rate']*totfc*data.loc[i,'dayrun_progress'])
#cal the remaining number of FC to fulfill the demand
data.loc[i,'demandfc_remain'] = data.loc[i,'demanddays_new'] - totfc
#re-cal the total demandays based on this condition
data.loc[i,'demanddays_new']= np.ceil(data.loc[i,'Demand_Qty_remain'] / (data.loc[i,'Loading_Rate']*data.loc[i,'demanddays_new'])) +data.loc[i,'dayrun_progress']
#cal the end date fc operate
data.loc[i,'FC_End_Date_change'] = data.loc[i,'FC_Start_Date_change'] + pd.to_timedelta(data.loc[i,'demanddays_new'], unit='D')
# Calculate Demurage cost
data.loc[i,'Demmurage_Day']=np.ceil((data.loc[i,'FC_End_Date_change'] - data.loc[i,'Departure_Date_change'])/np.timedelta64(1,'D'))
data.loc[i,'Demmurage_Cost']=data.loc[i,'Demurrage_Rate'] * data.loc[i,'Demmurage_Day']
# data.loc[i,'FC_gap_Date_change'] = data.loc[i,'Departure_Date_change'] + pd.to_timedelta(1, unit='D')
#if available fc < demand fc i and fc none available then
else:
#the fc must start till the previous mv finisih to load
data.loc[i,'FC_Start_Date_change'] = data.loc[i-1,'FC_End_Date_change'] + pd.to_timedelta(1, unit='D')
data.loc[i,'FC_End_Date_change'] = data.loc[i,'FC_Start_Date_change'] + pd.to_timedelta(data.loc[i,'demanddays_new'], unit='D')
# Calculate Demurage cost
data.loc[i,'Demmurage_Day']=np.ceil((data.loc[i,'FC_End_Date_change'] - data.loc[i,'Departure_Date_change'])/np.timedelta64(1,'D'))
data.loc[i,'Demmurage_Cost']=data.loc[i,'Demurrage_Rate'] * data.loc[i,'Demmurage_Day']
# data.loc[i,'FC_gap_Date_change'] = data.loc[i,'Departure_Date_change'] + pd.to_timedelta(1, unit='D')
totfc=3 #reset to initial total fc
else:
totfc = 3
data.loc[i,'FC_Start_Date_change'] = data.loc[i,'Arrival_Date_change']
data.loc[i,'FC_End_Date_change'] = data.loc[i,'Est_Departure_Date_change']
data.loc[i,'Demmurage_Day']=np.ceil((data.loc[i,'FC_End_Date_change'] - data.loc[i,'Departure_Date_change'])/np.timedelta64(1,'D'))
data.loc[i,'Demmurage_Cost']=data.loc[i,'Demurrage_Rate'] * data.loc[i,'Demmurage_Day']
# data.loc[i,'FC_gap_Date_change'] = data.loc[i,'Departure_Date_change'] + pd.to_timedelta(1, unit='D')
data.loc[data.Demmurage_Day<=0 ,'Demmurage_Day']=0
data.loc[data.Demmurage_Cost<=0 ,'Demmurage_Cost']=0
# data.loc[data.Demmurage_Cost<=0 ,'FC_gap_Date_change']=data.loc[data.Demmurage_Cost<=0 ,'FC_End_Date_change']
return data
### Call function
data=sim_demuragecost(totfc,data)
data
def gantt_fig3(data):
data3 = []
for row in data.itertuples():
data3.append(dict(Task=str(row.MV), Start=str(row.Arrival_Date_change),
Finish=str(row.Departure_Date_change), Resource='Plan'))
data3.append(dict(Task=str(row.MV), Start=str(row.FC_Start_Date_change),
Finish=str(row.FC_End_Date_change), Resource='Actual'))
fig = ff.create_gantt(data3, index_col='Resource', title='Gantt Chart', show_colorbar = True, group_tasks = True , height=500, width=1300 )
fig['layout'].update(legend=dict(traceorder='reversed'))
return fig
iplot(gantt_fig3(data))
data=pd.concat(datadone,data)
newtable=data
posttable=data
newtable.columns
newtable['Arrival_Date']=newtable.Arrival_Date_change
newtable['Departure_Date']=newtable.Departure_Date_change
tab=newtable[['MV', 'ETA', 'Arrival_Date', 'Laytime_Duration', 'Departure_Date',
'Demand_Qty', 'Loading_Rate', 'Price',
'FC_A', 'FC_B', 'FC_C', 'FC_D','FC_E', 'FC_F',
'Demmurage_Day', 'Demurrage_Rate', 'Demmurage_Cost']]
tab.to_excel('story_'+ story.value+'/story'+ story.value+'.xlsx',sheet_name='sample',engine='xlsxwriter',index=False)
data.drop(['demanddays'],axis=1,inplace=True)
data.rename(columns={'demanddays_new':'demanddays'},inplace=True)
print( 'Total demurage cost: USD ' +str(data.Demmurage_Cost.sum()))
button
data.dropna(axis=0, how='all', thresh=None, subset=None, inplace=True)
data=data.drop(['FC_A', 'FC_B', 'FC_C', 'FC_D','FC_E', 'FC_F'],axis=1)
data
return button, display(data),data;
for data_file in data_files:
if ".zip" in data_file:
data_file_name = data_file[:-4]
if extract_zip_files:
with ZipFile("%s/%s" % (DATA_DIR, data_file), 'r') as zipObj:
listOfFileNames = zipObj.namelist()
fileName = listOfFileNames[0]
zipObj.extractall("/tmp")
os.replace("/tmp/%s" % fileName,
"/tmp/%s.xls" % data_file_name)
xl = pd.ExcelFile("/tmp/%s.xls" % data_file_name)
sheet_name = xl.sheet_names[0]
df = pd.read_excel(xl,
sheet_name,
usecols=[NAME, DATE, INTEREST, LONG, SHORT])
name_list += list(df[NAME])
date_list += list(df[DATE])
interest_list += list(df[INTEREST])
long_list += list(df[LONG])
short_list += list(df[SHORT])
num_of_entries = len(name_list)
z_scores_one_year = []
z_scores_three_year = []
cwd = os.getcwd()
import requests
import csv
import json
import matplotlib.pyplot as plt
import seaborn as sns
'''
Åldersgrupp : Agegroup
Antal vaccinerade : Number of vaccinated
Andel vaccinerade : Proportion of vaccinated
Dosnummer : Dosenumber
'''
xls = pd.ExcelFile("https://fohm.maps.arcgis.com/sharing/rest/content/items/fc749115877443d29c2a49ea9eca77e9/data")
xls1 = pd.read_excel(xls, 'Vaccinerade ålder')
#print(xls1.columns)
# Let's drop unnamed column from the dataframe
df = xls1.drop("Unnamed: 5", axis=1)
#print(df.columns)
#Now we will seperate dose1 and dose2 of entire sweden
df_sweden_dose1 = df.loc[0:8]
print(df_sweden_dose1)
df_sweden_dose2 = df.loc[9:17]
#print(df_sweden_dose2)
import pandas
df = pandas.read_excel('712693030RPKUP4RX.xlsx')
header = df.iloc[2] #取得標題
df1 = df[3:].copy() #去除前三列
df1 = df1.rename(columns=header) #重置標題
df2 = df1.drop(columns=['縣市代碼', '村里代碼', '村里名稱', '村里代碼'], axis=1) #去除四行資料
df3 = df2.drop_duplicates() #移除重複資料
df3.to_csv('district.csv', encoding='big5', index=False)
from ics_data_clean import clean_data, clean_text_round1, process_data
from ics_train_classifier import train_
from plotly.graph_objs import Bar, Scatter
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# pickle load important files
category_id_df = pd.read_pickle('../pickle/ics/factorize/category_id_df.pkl')
id_to_category = dict(category_id_df[['category_id', 'category']].values)
with open('../pickle/ics/stop_words.pickle', 'rb') as f:
stop_words = pickle.load(f)
# training_database_table = pd.read_pickle('../pickle/ics/training_database_index.pkl')
# process list
process_list = pd.read_excel('../data/ics/processes.xlsx')
# load training data
df = pd.read_pickle('../pickle/ics/data_final.pkl')
# define vectorizers
tfidf = TfidfVectorizer(analyzer='word',
sublinear_tf=True,
norm='l2',
encoding='latin-1',
ngram_range=(1, 2),
stop_words=stop_words)
tfidf.fit(df.content)
cv = CountVectorizer(stop_words=stop_words)
tfidf_s = TfidfVectorizer(analyzer='word',
import pandas as pd
import numpy as np
import sys
#读取excel里的数据
df=pd.read_excel('E:/PythonStudy_Git/调用资料/file/菜品报表 (1).xlsx',sheet_name = 0)
#增加时间
df['时间']='2020-10-02'
df2=pd.read_excel('E:/PythonStudy_Git/调用资料/file/菜品报表 (2).xlsx',sheet_name = 0)
print(df2)
'''合并两表,how：连接方式:
有inner(根据条件,如同等id号的合并成一条记录
left左合并
right右合并
outer下合并
默认为inner；'''
df3=pd.merge(df,df2,how="outer")
print(df3)
