I have a dataframe that is called dfactual this dataframe has a column ForeCastEndDate, so
dfactual['ForeCastEndDate'] it contains:
311205
311205
This must be a date in the format 31-12-2005, but the current format is int64. I tried the following:
dfactual['ForeCastEndDate'] = pd.to_datetime(pd.Series(dfactual['ForecastEndDate']))
I tried also to add the format command to it, but it didn't work out the format stays the same, int64.
How should I do it?
You can't use to_datetime with dtypes that are not str so you need to convert the dtype using astype first and then you can use to_datetime and pass the format string:
In [154]:
df = pd.DataFrame({'ForecastEndDate':[311205]})
pd.to_datetime(df['ForecastEndDate'].astype(str), format='%d%m%y')
Out[154]:
0 2005-12-31
Name: ForecastEndDate, dtype: datetime64[ns]
Related
I want to convert values in entire column from strings to datetime objects, but I can't accomplish it with this code which works on solo strings i.e. (if I add .iloc[] and specify the index):
price_df_higher_interval['DateTime'] = datetime.datetime.strptime(price_df_higher_interval['DateTime'],
'%Y-%m-%d %H:%M:%S')
Also I would like to ommit looping through the dataframe, but I don't know if that won't be necessery.
Thank you for your help :)
You could use the pd.to_datetime function.
df = pd.DataFrame({"str_date": ["2023-01-01 12:13:21", "2023-01-02 13:10:24 "]})
df["date"] = pd.to_datetime(df["str_date"], format="%Y-%m-%d %H:%M:%S")
df.dtypes
str_date object
date datetime64[ns]
dtype: object
20160116
Suppose this is the data with datatype integer in a column and now I want to convert it like 2016/01/16 or 2016-01-16 and datatype as date. My column name is system and dataframe is df. How can I do that?
I tried using many date format function but It was not good enough to achieve the answer.
convert using to_datetime, provide the format
then convert to the format of your desire
pd.to_datetime(df['dte'], format='%Y%m%d').dt.strftime('%Y/%m/%d')
0 2016/01/06
Name: dte, dtype: object
Using str.replace we can try:
df["date"] = df["system"].astype(str).str.replace(r'(\d{4})(\d{2})(\d{2})', r'\1/\2/\3', regex=True)
I have a csv file with a column "graduated" which either shows the date of graduation, or 0 if there is no graduation yet.
df.dtypes return 'object' for this column, I want to turn all the dates into a '1' (indicating that the person in that column graduated). How can I do that ?
Use pandas.to_datetime to convert dates and convert to boolean series. Then, cast it to int to get the desired result.
pd.to_datetime(df.graduated, errors='coerce').notnull().astype(int)
The following data below is from a pandas series, but I need the date converted to DatetimeIndex like this format: 2020-08-17. The index of this series should be a pd.DatetimeIndex. What are some ways to convert it as such?
8/17/20 14082780.0
8/18/20 14277100.0
8/19/20 14483216.0
8/20/20 14685442.0
8/21/20 14886403.0
Length: 212, dtype: float64
Just change the index to be as type of datetime:
df.index = pd.to_datetime(df.index)
More generally for a non-index column:
df['Date']= pd.to_datetime(df['Date'])
I have a column from a pandas Dataframe that I want to use as input for np.busday_count:
np.busday_count(df['date_from'].tolist(), df['date_to_plus_one'].tolist(), weekmask='1000000')
I have always use .tolist() but since one of the last updates this results in an error:
> TypeError: Iterator operand 0 dtype could not be cast from
> dtype('<M8[us]') to dtype('<M8[D]') according to the rule 'safe'
The column df['date_from']is of type dtype: datetime64[ns].
Any tips or solution for this?
try Using
df['date_from'].date()
The column df['date_from'] with datatype dtype: datetime64[ns] contains data like
2018-04-06 00:00:00 its a timestamp
But np.busyday_count takes datetime.date as input like "2018-04-06"