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Pandas read_excel doesn't parse dates correctly - returns a constant date instead
(1 answer)
Convert Excel style date with pandas
(3 answers)
Closed 1 year ago.
I want to read xlsb file in pandas.
and I have 3 datetime column
1st column format is (2021-5-31 01:20:23 )
2nd column format (total time) is ( 01:20:23 )
3st column format is ( 01:20:23 am )
but when I am reading the file I am getting column 46090.0
Is there any method that can read the excel column as it is?
Related
This question already has answers here:
Convert Pandas Column to DateTime
(8 answers)
Closed 4 months ago.
I'm kinda new in Python.. help me find the solution.. I would like to convert iso-like date such as "2022-10-28T08:00:19Z" in a dataframe column into readable like '2022-10-28' or '28-10'2022' so I can filter and count the data if date >= '01-01-2022' then show the data.
this is the example of data:
date_published
2022-10-28T08:00:19Z
2022-10-28T05:00:08Z
2022-10-28T03:00:13Z
2022-10-27T13:55:38Z
2022-10-27T13:21:36Z
df['date_published']=pd.to_datetime(df['date_published']).dt.date
This question already has answers here:
How to combine multiple rows into a single row with pandas [duplicate]
(1 answer)
Concatenate strings from several rows using Pandas groupby
(8 answers)
Closed 1 year ago.
I have this dataframe of tweets collected with the date they were posted. I would like to know if there is any way to group these tweets by date?
For example all tweets from one day X would be on the same line in the dataframe
This question already has answers here:
Convert pyspark string to date format
(6 answers)
Closed 2 years ago.
I have a dataset which contains Multiple columns and rows.
Currently, it's in String type And, I wanted to convert to a date-time format for further task.
I tried this below code which returns null
df = df.withColumn('Date_Time',df['Date_Time'].cast(TimestampType()))
df.show()
I tried some of the solutions from here, but none of them is working all, in the end, returns me null.
Convert pyspark string to date format
Since your date format is non-standard, you need to use to_timestamp and specify the corresponding format:
import pyspark.sql.functions as F
df2 = df.withColumn('Date_Time', F.to_timestamp('Date_Time', 'dd/MM/yyyy hh:mm:ss a'))
This question already has answers here:
how to sort pandas dataframe from one column
(13 answers)
Closed 2 years ago.
I have a column in my csv file that I want to have sorted by the datetime. It's in the format like 2020-10-06 03:28:00. I tried doing it like this but nothing seems to have happened.
df = pd.read_csv('data.csv')
df = df.sort_index()
df.to_csv('btc.csv', index= False)
I need to have that index= False in the .to_csv so that it is formatted properly for later so I can't remove that if that is causing an issue. The dtime is my first column in the csv file and the second column is a unix timestamp so I could also use that if it would work better.
sort_values(by=column_name) to sort pandas. DataFrame by the contents of a column named column_name . Before doing this, the data in the column must be converted to datetime if it is in another format using pandas. to_datetime(arg) with arg as the column of dates.
This question already has an answer here:
Can I parse dates in different formats?
(1 answer)
Closed 5 years ago.
I have some data in csv file which has some entries in the MM/DD/YYYY format and some entries in the DD-MM-YYYY format. I would like to read this column of entries and store it as a new column in a pandas dataframe? How would I go about this?
Example:
Entry Sampling Date
1 01-10-2004
2 01-13-2004
3 16/1/2004
I would like to convert the first two rows' date format to that in the third row.
Use the datetime module, define a function and then apply it to your column
import datetime.datetime
def read_date(string):
if '/' in entry:
date = datetime.datetime.strptime(string,'%m/%d/%Y')
elif '-' in entry:
date = datetime.datetime.strptime(string, '%d-%m-%Y')
return date
# If df is your dataframe
df['newdate'] = df['Sampling Date'].apply(read_date)