Python 熊猫：将日期“对象”转换为整数
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Pandas: convert date 'object' to int
提问by jabba
I have a Pandas dataframe and I need to convert a column with dates to int but unfortunately all the given solutions end up with errors (below)
我有一个 Pandas 数据框，我需要将带有日期的列转换为 int 但不幸的是所有给定的解决方案最终都出现错误（如下）
test_df.info()
<class 'pandas.core.frame.DataFrame'>
Data columns (total 4 columns):
Date 1505 non-null object
Avg 1505 non-null float64
TotalVol 1505 non-null float64
Ranked 1505 non-null int32
dtypes: float64(2), int32(1), object(1)
sample data:
样本数据：
Date Avg TotalVol Ranked
0 2014-03-29 4400.000000 0.011364 1
1 2014-03-30 1495.785714 4.309310 1
2 2014-03-31 1595.666667 0.298571 1
3 2014-04-01 1523.166667 0.270000 1
4 2014-04-02 1511.428571 0.523792 1
I think that I've tried everything but nothing works
我想我已经尝试了一切，但没有任何效果
test_df['Date'].astype(int):
TypeError: int() argument must be a string, a bytes-like object or a number, not 'datetime.date'
类型错误：int() 参数必须是字符串、类似字节的对象或数字，而不是“datetime.date”
test_df['Date']=pd.to_numeric(test_df['Date']):
TypeError: Invalid object type at position 0
类型错误：位置 0 处的对象类型无效
test_df['Date'].astype(str).astype(int):
ValueError: invalid literal for int() with base 10: '2014-03-29'
ValueError：int() 的无效文字，基数为 10：'2014-03-29'
test_df['Date'].apply(pd.to_numeric, errors='coerce'):
Converts the entire column to NaNs
将整列转换为 NaN
回答by Neroksi
The reason why test_df['Date'].astype(int)gives you an error is that your dates still contain hyphens "-". First suppress them by doing test_df['Date'].str.replace("-",""), then you can apply your first method to the resulting series. So the whole solution would be :
test_df['Date'].astype(int)给你一个错误的原因是你的日期仍然包含连字符“ -”。首先通过执行 来抑制它们test_df['Date'].str.replace("-","")，然后您可以将第一种方法应用于结果系列。所以整个解决方案是：
test_df['Date'].str.replace("-","").astype(int)Note that this won't work if your "Date" column is not a string object, typically when Pandas has already parsed your series as TimeStamp. In this case you can use :
test_df['Date'].str.replace("-","").astype(int)请注意，如果您的“日期”列不是字符串对象，这将不起作用，通常是当 Pandas 已经将您的系列解析为时间戳时。在这种情况下，您可以使用：
test_df['Date'].dt.strftime("%Y%m%d").astype(int)
回答by Rakesh
Looks like you need pd.to_datetime().dt.strftime("%Y%m%d").
看起来你需要pd.to_datetime().dt.strftime("%Y%m%d").
Demo:
演示：
import pandas as pd
df = pd.DataFrame({"Date": ["2014-03-29", "2014-03-30", "2014-03-31"]})
df["Date"] = pd.to_datetime(df["Date"]).dt.strftime("%Y%m%d")
print( df )
Output:
输出：
Date
0 20140329
1 20140330
2 20140331
回答by msolomon87
This should work
这应该工作
df['Date'] = pd.to_numeric(df.Date.str.replace('-',''))
print(df['Date'])
0 20140329
1 20140330
2 20140331
3 20140401
4 20140402
