Hello I have a dataframe containing a date column I would like to loop through these dates and compare it to the current date to see if any entry is today. I tried converting the column to a list using the tolist() method but it outputted not the date but rather "Timestamp('2022-08-02 00:00:00')" however my column only contains dates formatted as %Y-%m-%d as you can see in the image.
dataframe
Assuming that your Dataframe is called df, here's a possible way of solving your issue:
df.loc[df.Date == pd.Timestamp.now().date().strftime('%Y-%m-%d')]
I think it's a straightforward solution, you filter your dataframe by "Date" and compare to the date part of "today's date" while maintaining the correct format of y-m-d.
Related
I want to add a new column 'timestamp' in the existing python dataframe. I tried the code below,
df["timestamp"]=datetime.datetime.now().replace(microsecond=0).replace(second=0).isoformat()+"Z"
But I got the same timestamp for every rows. Actually I need a new column, which contains a series of timestamps. Which starts from a particular timestamp.
something like this maybe?:
df["timestamp"] = pd.date_range(datetime.datetime.now(), freq='1s', periods=len(df)).strftime('%Y-%m-%dT%H-%M-%SZ')
A few rows of my dataframe
The third column shows the time of completion of my data. Ideally, I'd want the second row to just show the date, removing the second half of the elements, but I'm not sure how to change the elements. I was able to change the (second) column of strings into a column of floats without the pound symbol in order to find the sum of costs. However, this column has no specific keyword I just select for all of the elements to remove.
Second part of my question is is it is possible to easy create another dataframe that contains 2021-05-xx or 2021-06-xx. I know there's a way to make another dataframe selecting certain rows like the top 15 or bottom 7. But I don't know if there's a way to make a dataframe finding what I mentioned. I'm thinking it follows the Series.str.contains(), but it seems like when I put '2021-05' in the (), it shows a entire dataframe of False's.
Extracting just the date and ignoring the time from the datetime column can be done by changing the formatting of the column.
df['date'] = pd.to_datetime(df['date']).dt.date
To the second part of the question about creating a new dataframe that is filtered down to only contain rows between 2021-05-xx and 2021-06-xx, we can use pandas filtering.
df_filtered = df[(df['date'] >= pd.to_datetime('2021-05-01')) & (df['date'] <= pd.to_datetime('2021-06-30'))]
Here we take advantage of two things: 1) Pandas making it easy to compare the chronology of different dates using numeric operators. 2) Us knowing that any date that contains 2021-05-xx or 2021-06-xx must come on/after the first day of May and on/before the last day of June.
There are also a few GUI's that make it easy to change the formatting of columns and to filter data without actually having to write the code yourself. I'm the creator of one of these tools, Mito. To filter dates in Mito, you can just enter the dates using our calendar input fields and Mito will generate the equivalent pandas code for you!
I have a data frame named inject. I have made a column name date as the index of the data frame inject. I want to find the rows corresponding to a particular date. The data type of column date is datetime.
inject_2017["2017-04-20"]
Writing this code throwing me an error.
Try inject_2017.loc["2017-04-20"]
This way you can select the row (or group of rows) with the corresponding datetime index.
I have a DataFrame with a date_time column. The date_time column contains a date and time. I also managed to convert the column to a datetime object.
I want to create a new DataFrame containing all the rows of a specific DAY.
I managed to do it when I set the date column as the index and used the "loc" method.
Is there a way to do it even if the date column is not set as the index? I only found a method which returns the rows between two days.
You can use groupby() function. Let's say your dataframe is df,
df_group = df.groupby('Date') # assuming the column containing dates is called Date.
Now you can access rows of any date by passing the date in the get_group function,
df_group.get_group('date_here')
I am trying to get some data through the API from quandl but the date column doesn't seem to work the same level as the other columns. E.g. when I use the following code:
data = quandl.get("WIKI/KO", trim_start = "2000-12-12", trim_end =
"2014-12-30", authtoken=quandl.ApiConfig.api_key)
print(data['Open'])
I end up with the below result
Date
2000-12-12 57.69
2000-12-13 57.75
2000-12-14 56.00
2000-12-15 55.00
2000-12-18 54.00
E.g. date appearing along with the 'Open' column. And when I try to directly include Date like this:
print(data[['Open','Date']]),
it says Date doesn't exist as a column. So I have two questions: (1) How do I make Date an actual column and (2) How do I select only the 'Open' column (and thus not the dates).
Thanks in advance
Why print(data['Open']) show dates even though Date is not a column:
quandle.get returns a Pandas DataFrame, whose index is a DatetimeIndex.
Thus, to access the dates you would use data.index instead of data['Date'].
(1) How do I make Date an actual column
If you wish to make the DatetimeIndex into a column, call reset_index:
data = data.reset_index()
print(data[['Open', 'Date']])
(2) How do I select only the 'Open' column (and thus not the dates)
To obtain a NumPy array of values without the index, use data['Open'].values.
(All Pandas Series and DataFrames have Indexs (that's Pandas' raison d'être!), so the only way obtain the values without the index is to convert the Series or DataFrame to a different kind of object, like a NumPy array.)