所以,在我的第一个文件的末尾;我们打电话给/ file.py
。
def get_excel_data(self):
"""Places excel data into pandas dataframe"""
# excel_data = pandas.read_excel(self.find_file())
for extracted_archive in self.find_file():
excel_data = pandas.read_excel(extracted_archive)
# print(excel_data)
columns = pandas.DataFrame(columns=excel_data.columns.tolist())
excel_data = pandas.concat([excel_data, columns])
excel_data.columns = excel_data.columns.str.strip()
excel_data.columns = excel_data.columns.str.replace("/", "_")
excel_data.columns = excel_data.columns.str.replace(" ", "_")
total_records = 0
num_valid_records = 0
num_invalid_records = 0
for row in excel_data.itertuples():
mrn = row.MRN
total_records += 1
if mrn in ("", " ", "N/A", "NaT", "NaN", None) or math.isnan(mrn):
# print(f"Invalid record: {row}")
num_invalid_records += 1
# total_invalid = num_invalid_records + dup_count
excel_data = excel_data.drop(excel_data.index[row.Index])
# continue
else:
# print(mrn) # outputs all MRN ids
for row in excel_data.itertuples():
num_valid_records += 1
continue
with open("./logs/metrics.csv", "a", newline="\n") as f:
csv_writer = DictWriter(f, ['date', 'total_records', 'processed', 'skipped', 'success_rate'])
# csv_writer.writeheader()
currentDT = datetime.datetime.now()
success_rate = num_valid_records / total_records * 100
csv_writer.writerow(dict(date=currentDT,
total_records=total_records,
processed=num_valid_records,
skipped=num_invalid_records,
success_rate=num_valid_records / total_records * 100))
return self.clean_data_frame(excel_data)
def clean_data_frame(self, data_frame):
"""Cleans up dataframes"""
for col in data_frame.columns:
if "date" in col.lower():
data_frame[col] = pandas.to_datetime(data_frame[col],
errors='coerce', infer_datetime_format=True)
data_frame[col] = data_frame[col].dt.date
data_frame['MRN'] = data_frame['MRN'].astype(int).astype(str)
return data_frame
def get_mapping_data(self):
map_data = pandas.read_excel(config.MAPPING_DOC, sheet_name='main')
columns = pandas.DataFrame(columns=map_data.columns.tolist())
return pandas.concat([map_data, columns])
在我的第二个文件中,我想保持最终状态;然后做另一次迭代.... second_file.py
def process_records(self, records, map_data, completed=None, errors=None):
"""Code to execute after webdriver initialization."""
series_not_null = False
try:
num_attempt = 0
for record in data_frame.itertuples(): # not working
print(record)
series_not_null = True
mrn = record.MRN
self.navigate_to_search(num_attempt)
self.navigate_to_member(mrn)
self.navigate_to_assessment()
self.add_assessment(record, map_data)
self.driver.switch_to.parent_frame() # not working
sleep(.5)
error_flag = self.close_member_tab(self.driver, mrn, error_flag)
except Exception as exc:
if series_not_null:
errors = self.process_series_error(exc)
return completed, error
都有import pandas
您可以将数据帧保存在这样的pickle文件中。值得注意的是,您可以将大部分内容存储在pickle文件中。这里有一些信息的链接:pickle info
import pandas as pd
import pickle
x = pd.DataFrame({'a':[1,2,3],'b':[4,5,6],'c':[7,8,9]})
#this will create a file called pickledata.p that will store the data frame
with open('pickledata.p', 'wb') as fh: #notice that you need the 'wb' for the dump
pickle.dump(x, fh)
#to load the file do this
with open('pickledata.p', 'rb') as fh: #you need to use 'rb' to read
df = pickle.load(fh)
#you can now use df like a normal dataframe
print(df)
你真的不需要'.p'扩展名为pickle文件,我喜欢它。
所以你在脚本1的末尾保存你的数据帧,然后在脚本2的开头加载它。
使用Dataframe.to_pickle
和pandas.read_pickle
:
坚持下去
df.to_pickle('./dataframe.pkl')
载入
df = pd.read_pickle('./dataframe.pkl')