如何用Pandas read_csv按指定时间段筛选CSV文件行？

The issue you're facing comes down to two key problems in your code:

1. You're trying to index the Price series using dates that aren't set as the DataFrame's index.
2. There's a case mismatch between your CSV column name (Price) and how you're accessing it (abc1.price looks for a lowercase price column).

Here are two straightforward solutions to resolve this:

Solution 1: Set Date as the Index When Reading the CSV #

By making the Date column your DataFrame's index, you can easily slice the data using date ranges:

import pandas as pd
from datetime import datetime

filesource1 = "your_file.csv"
# Read CSV, parse dates, and set Date as the index
abc1 = pd.read_csv(filesource1, parse_dates=['Date'], index_col='Date')
# Access the Price column correctly (matches CSV column name)
abc2 = abc1['Price']

startDate = datetime(2014, 8, 1)
endDate = datetime(2018, 3, 1)

# Slice the series using the date range
abc3 = abc2.loc[startDate:endDate]
print(abc3.head())

Solution 2: Use Boolean Filtering (No Index Change Needed) #

If you prefer to keep the default integer index, you can filter rows using boolean conditions on the Date column:

import pandas as pd
from datetime import datetime

filesource1 = "your_file.csv"
abc1 = pd.read_csv(filesource1, parse_dates=['Date'])

startDate = datetime(2014, 8, 1)
endDate = datetime(2018, 3, 1)

# Filter rows where Date is within your desired range
filtered_rows = abc1[(abc1['Date'] >= startDate) & (abc1['Date'] <= endDate)]
# Extract the Price column from the filtered data
abc3 = filtered_rows['Price']
print(abc3.head())

Key Notes: #

• Always use bracket notation (abc1['Price']) instead of dot notation (abc1.price) to access columns, especially if the column name has uppercase letters or spaces. This avoids case-sensitivity issues.
• The parse_dates=['Date'] argument ensures pandas recognizes the Date column as datetime objects, which is essential for date-based filtering.

内容的提问来源于stack exchange，提问作者Mike