Attributeerror: Can Only Use .Dt Accessor with Datetimelike Values

AttributeError: Can only use .dt accessor with datetimelike values

Your problem here is that to_datetime silently failed so the dtype remained as str/object, if you set param errors='coerce' then if the conversion fails for any particular string then those rows are set to NaT.

df['Date'] = pd.to_datetime(df['Date'], errors='coerce')

So you need to find out what is wrong with those specific row values.

See the docs

AttributeError: Can only use .dt accessor with datetimelike values but cells contain date time

The solution is to add utc=True as follows:

import pandas as pd
df = pd.read_csv("my_csv_content_below.csv")
df["timestamp"] = pd.to_datetime(df["timestamp"], utc=True)
df["timestamp"] = df["timestamp"].dt.tz_convert(timezone_for_plot)

error Can only use .dt accessor with datetimelike values

Seems like "Date and Time" is not a column with a datetime format. You could try to transform that column with this before that line:

output_df['Date and Time'] = pd.to_datetime(output_df['Date and Time'])

Can only use .dt accessor with datetimelike values pandas error

Your data may contain different timezones, like this:

key      system      impl_date
1        madison     2021-01-27T13:16:18.000-0600
2        madison     2021-01-27T13:15:04.000-0600
3        lexington   2021-01-27T13:08:27.000-0600
5        park        2021-01-27T13:05:42.000-0500   # here

Option is to pass utc=True to to_datetime:

pd.to_datetime(df['impl_date'], errors='coerce', utc=True).dt.strftime('%Y-%m-%d')

And you get:

0    2021-01-27
1    2021-01-27
2    2021-01-27
3    2021-01-27
Name: impl_date, dtype: object

Can only use .dt accessor with datetimelike values

Use format '%Y-%m-%d' and use .dt.strftime("%Y%m")

Ex:

import pandas as pd
testdata['prd_exp_dt'] = pd.to_datetime(testdata['prd_exp_dt'] ,errors = 'coerce',format = '%Y-%m-%d').dt.strftime("%Y%m")

Attribute error: json file processing .dt accessor with datetimelike values

>>> df
   plant_date
0         NaN
1  2021-11-12
>>> df.loc[1, 'plant_date'] = pd.to_datetime(df.loc[1, 'plant_date'], errors='coerce', format='%Y-%m-%d')
>>> df
            plant_date
0                  NaN
1  2021-11-12 00:00:00

Due to how you're creating your dataframe - it being prepopulated with np.nan and converting individial "rows" to datetime - the "type" of the column will be object (because nan is of type float - so the column contains "mixed types")

>>> df.dtypes
plant_date    object
--------------^^^^^^ 
dtype: object
>>> df['plant_date'].dt
AttributeError: Can only use .dt accessor with datetimelike values

In order to use .dt you need the column to be of type datetime

>>> df
   plant_date
0         NaN
1  2021-11-12
>>> df.dtypes
plant_date    object
dtype: object

The simplest way is probably just to call to_datetime() on the whole column at once - either on out in your function - or on obs_df after you create it.

>>> df['plant_date'] = pd.to_datetime(df['plant_date'])
>>> df
  plant_date
0        NaT
1 2021-11-12
>>> df.dtypes
plant_date    datetime64[ns]
dtype: object

Now you can use .dt without error

>>> df['plant_date'].dt
<pandas.core.indexes.accessors.DatetimeProperties object at 0x1210fe160>

The harv_date column should have the same issue.

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