你没有说你是如何得到这个列表的，但它看起来很像嵌套列表。
data = [
['erinil01', 'Oppstart', '', 'Startet programmet', '06/07/21', '12:48:54', '2021', '2'],
['erinil01', 'Oppstart', '', 'Startet programmet', '06/07/21', '12:56:49', '2021', '2'],
['erinil01', 'Prosjektadmin', '920208', 'Lastet prosjektet', '06/07/21', '12:59:09', '2021', '2'],
['erinil01', 'Prosjektadmin', '920208', 'Lagret prosjektet', '06/07/21', '12:59:17', '2021', '2'],
['erh4021', 'Oppstart', '', 'Startet programmet', '06/07/21', '13:02:38', '2021', '2'],
['erinil01', 'Prosjektadmin', '921106', 'Lagt til nytt prosjekt', '06/07/21', '13:06:45', '2021', '2'],
['erinil01', 'Prosjektadmin', '921107', 'Lagt til nytt prosjekt', '06/07/21', '13:07:02', '2021', '2'],
['erinil01', 'Prosjektadmin', '921106', 'Lastet prosjektet', '06/07/21', '13:07:08', '2021', '2'],
]
对于嵌套列表，您必须使用 for-loop 单独处理每一行。
并且对于每一行，您都可以使用索引来检查值。
这将获取所有具有空值Project 的行，其索引为[2]
filtered_data = []
for row in data:
if not row[2]:
#print('empty:', row)
filtered_data.append(row)
print('--- filtered_data ---')
for row in filtered_data:
print(row)
对于更复杂的过滤器，您必须创建更复杂的if。
为了使其更通用，您可以创建获取单行的函数，如果您想保留这一行，则返回 True 或 False。
def selected(row):
#if not row[2]:
# return True
#else:
# return False
# shorter
return not row[2]
filtered_data = []
for row in data:
if selected(row):
#print('empty:', row)
filtered_data.append(row)
然后你甚至可以将其简化为列表理解
filtered_data = [row for row in data if selected(row)]
或使用函数filter()
filtered_data = list(filter(selected, data))
这样你可以创建不同的函数selected()来组合过滤器。
filtered_data = list(filter(selected_1, data))
filtered_data = list(filter(selected_2, filtered_data))
filtered_data = list(filter(selected_3, filtered_data))
顺便说一句：
如果您从数据库中获取数据，那么您可以在从数据库中获取数据时直接使用SQL query 过滤数据。
如果您可以将数据保存在 pandas.DataFrame 中，那么您可以使用列名称 Username, Function, Project, Description, Date, Time, Year, Version 对其进行过滤。
编辑：
最小的工作示例
data = [
['erinil01', 'Oppstart', '', 'Startet programmet', '06/07/21', '12:48:54', '2021', '2'],
['erinil01', 'Oppstart', '', 'Startet programmet', '06/07/21', '12:56:49', '2021', '2'],
['erinil01', 'Prosjektadmin', '920208', 'Lastet prosjektet', '06/07/21', '12:59:09', '2021', '2'],
['erinil01', 'Prosjektadmin', '920208', 'Lagret prosjektet', '06/07/21', '12:59:17', '2021', '2'],
['erh4021', 'Oppstart', '', 'Startet programmet', '06/07/21', '13:02:38', '2021', '2'],
['erinil01', 'Prosjektadmin', '921106', 'Lagt til nytt prosjekt', '06/07/21', '13:06:45', '2021', '2'],
['erinil01', 'Prosjektadmin', '921107', 'Lagt til nytt prosjekt', '06/07/21', '13:07:02', '2021', '2'],
['erinil01', 'Prosjektadmin', '921106', 'Lastet prosjektet', '06/07/21', '13:07:08', '2021', '2'],
]
# --- version 1 ---
filtered_data = []
for row in data:
if (not row[2]) or (int(row[2]) > 920208):
#print('empty:', row)
filtered_data.append(row)
print('--- filtered_data ---')
for row in filtered_data:
print(row)
# --- version 2 ---
def selected(row):
#if (not row[2]) or (int(row[2]) > 920208):
# return True
#else:
# return False
# shorter
return (not row[2]) or (int(row[2]) > 920208)
def selected_1(row):
return not row[2]
def selected_2(row):
return int(row[2]) > 920208
filtered_data = []
for row in data:
if selected_1(row) or selected_2(row):
#if selected(row):
#print('empty:', row)
filtered_data.append(row)
print('--- filtered_data ---')
for row in filtered_data:
print(row)
# --- version 3 ---
def selected(row):
return (not row[2]) or (int(row[2]) > 920208)
def selected_1(row):
return not row[2]
def selected_2(row):
return int(row[2]) > 920208
filtered_data = [row for row in data if selected(row)]
filtered_data = [row for row in data if selected_1(row) or selected_2(row)]
print('--- filtered_data ---')
for row in filtered_data:
print(row)
# --- version 4 ---
def selected(row):
return (not row[2]) or (int(row[2]) > 920208)
def selected_1(row):
return not row[2]
def selected_2(row):
return int(row[2]) > 920208
#filtered_data = list(filter(selected, data))
filtered_data = list(filter(lambda row:selected_1(row) or selected_2(row), data))
print('--- filtered_data ---')
for row in filtered_data:
print(row)
编辑：
与pandas类似
data = [
['erinil01', 'Oppstart', '', 'Startet programmet', '06/07/21', '12:48:54', '2021', '2'],
['erinil01', 'Oppstart', '', 'Startet programmet', '06/07/21', '12:56:49', '2021', '2'],
['erinil01', 'Prosjektadmin', '920208', 'Lastet prosjektet', '06/07/21', '12:59:09', '2021', '2'],
['erinil01', 'Prosjektadmin', '920208', 'Lagret prosjektet', '06/07/21', '12:59:17', '2021', '2'],
['erh4021', 'Oppstart', '', 'Startet programmet', '06/07/21', '13:02:38', '2021', '2'],
['erinil01', 'Prosjektadmin', '921106', 'Lagt til nytt prosjekt', '06/07/21', '13:06:45', '2021', '2'],
['erinil01', 'Prosjektadmin', '921107', 'Lagt til nytt prosjekt', '06/07/21', '13:07:02', '2021', '2'],
['erinil01', 'Prosjektadmin', '921106', 'Lastet prosjektet', '06/07/21', '13:07:08', '2021', '2'],
]
import pandas as pd
import numpy as np
df = pd.DataFrame(data, columns=['Username', 'Function', 'Project', 'Description', 'Date', 'Time', 'Year', 'Version'])
df = df.replace(r'', np.nan) # to compare empty string with `float` value `920208`
print(df)
mask1 = df['Project'].isnull() # detect `np.nan`
#print(mask1)
mask2 = (df['Project'].astype(float) > 920208)
#print(mask2)
filtered_data = df[ mask1 | mask2 ] # `|` means `or` , `&` means `and`
print('--- filtered_data ---')
print(filtered_data)