通過日期列過濾熊貓中的數據框

Question

數據在以下鏈接中： http : //www.fdic.gov/bank/individual/failed/banklist.html

我只想要2017年關閉的銀行。我該如何在Pandas中這樣做？

failed_banks= pd.read_html('http://www.fdic.gov/bank/individual/failed/banklist.html')
failed_banks[0]

這些代碼行提取所需的結果后，我該怎么辦？

Answer 1

理想情況下，您將使用

# assuming pandas successfully parsed this column as datetime object
# and pandas version >= 0.16
failed_banks= pd.read_html('http://www.fdic.gov/bank/individual/failed/banklist.html')[0]
failed_banks = failed_banks[failed_banks['Closing Date'].dt.year == 2017]

但是熊貓不能正確地將“ Closing Date解析為日期對象，因此我們需要自己解析：

failed_banks = pd.read_html('http://www.fdic.gov/bank/individual/failed/banklist.html')[0]

def parse_date_strings(date_str):
    return int(date_str.split(', ')[-1]) == 2017

failed_banks = failed_banks[failed_banks['Closing Date'].apply(parse_date_strings)]

Answer 2

這樣的事情應該工作

提取關閉年份。

# using pd.to_datetime
closing_year = pd.to_datetime(failed_banks[0]['Updated Date']).apply(lambda x: x.year)
# or by splitting the line
closing_year = failed_banks[0]['Updated Date'].apply(lambda x: x.split(', ')[1])

並選擇。

failed_banks[0][closing_year=='2017']