In my data frame I have a column which contains timestamps. Now these timestamps are in the format(yyyy-mm-dd hh:mm:ss) and I want to change them to (dd--mm-yyyy hh:mm:ss). I have tried to do so but only the first row is changing properly and the rest of the rows are converting to epoch time i think.
Snapshot of Dataframe
what I tried
the other way I tried
As you can see only the first row is changing whereas the other rows are not. Please help guys!!!
I believe is because of the data type of your column in pandas. If you want to follow your previous attempts, you could just create a new column and fill the data as a string like this:
df_sch["UTC Formatted"] = [datetime.datetime.strftime(entity, "%d-%m-%Y %H:%M%S") for entity in df_sch["UTC"]]
In this sense the data will be stored as string! Hope this helps!
You can try with this instruction:
df_sch['UTC'] = df_sch["UTC"].dt.strftime('%d-%m-%Y %H:%M:%S')
This will convert all UTC column values in your dataframe with the wanted format
Related
enter image description here[enter image description here][2]I am having trouble interpolating my missing values. I am using the following code to interpolate
df=pd.read_csv(filename, delimiter=',')
#Interpolating the nan values
df.set_index(df['Date'],inplace=True)
df2=df.interpolate(method='time')
Water=(df2['Water'])
Oil=(df2['Oil'])
Gas=(df2['Gas'])
Whenever I run my code I get the following message: "time-weighted interpolation only works on Series or DataFrames with a DatetimeIndex"
My Data consist of several columns with a header. The first column is named Date and all the rows look similar to this 12/31/2009. I am new to python and time series in general. Any tips will help.
Sample of CSV file
Try this, assuming the first column of your csv is the one with date strings:
df = pd.read_csv(filename, index_col=0, parse_dates=[0], infer_datetime_format=True)
df2 = df.interpolate(method='time', limit_direction='both')
It theoretically should 1) convert your first column into actual datetime objects, and 2) set the index of the dataframe to that datetime column, all in one step. You can optionally include the infer_datetime_format=True argument. If your datetime format is a standard format, it can help speed up parsing by quite a bit.
The limit_direction='both' should back fill any NaNs in the first row, but because you haven't provided a copy-paste-able sample of your data, I cannot confirm on my end.
Reading the documentation can be incredibly helpful and can usually answer questions faster than you'll get answers from Stack Overflow!
I am trying to create a new data frame that excludes all timestamps bigger/later than 12:00:00. I tried multiple approaches but I keep on having an issue. The time was previously a datetime column that I change to 2 columns, a date and time column (format datetime.time)
Code:
Issue thrown out:
Do you have any suggestions to be able to do this properly?
Alright the solution:
change the Datetype from datetime.time to Datetime
Use the between method by setting the Datetime column as the index and setting the range
The result is a subset of the dataframe based on the needed time frame.
Hi I have looked but on stackoverflow and not found a solution for my problem. Any help highly appeciated.
After importing a csv I noticed that all the types of the columns are object and not float.
My goal is to convert all the columns but the YEAR column to float. I have read that you first have to strip the columns for taking blanks out and then also convert NaNs to 0 and then try to convert strings to floats. But in the code below I'm getting an error.
My code in Jupyter notes is:
And I get the following error.
How do I have to change the code.
All the columns but the YEAR column have to be set to float.
If you can help me set the column Year to datetime that would be also very nice. But my main problem is getting the data right so I can start making calculations.
Thanks
Runy
Easiest would be
df = df.astype(float)
df['YEAR'] = df['YEAR'].astype(int)
Also, your code fails because you have two columns with the same name BBPWN, so when you do df['BBPWN'], you will get a dataframe with those two columns. Then, df['BBPWN'].str will fail.
I have a large dataset with multiple date columns that I need to clean up, mostly by removing the time stamp since it is all 00:00:00. I want to write a function that collects all columns if type is datetime, then format all of them instead of having to attack one each.
I figured it out. This is what I came up with and it works for me:
def tidy_dates(df):
for col in df.select_dtypes(include="datetime64[ns, UTC]"):
df[col] = df[col].dt.strftime("%Y-%m-%d")
return df
I'm trying to read from an Excel file that gets converted to python and then gets split into numbers (Integers and floats) and everything else. There are numerous columns of different types.
I currently bring in the data with
pd.read_excel
and then split the data up with
DataFrame.select_dtypes("number")
When users upload a time (so 12:30:00) they expect for it to be recognized as a time. However python (currently) treats it as dtype object.
If I specify the column with parse_dates then it works, however since I don't know what the data is in advance I ideally want this to be done automatically. I`ve tried setting parse_dates = True however it doesn't seem to make a difference.
I'm not sure if there is a way to recognize the datatime after the file is uploaded. Again however I would want this to be done without having to specify the column (so anything that can be converted is)
Many Thanks
If your data contains only one column with dtype object (I assume it is a string) you can do the following:
1) filter the column with dtype object
import pandas as pd
datatime_col = df.select_dtypes(object)
2) convert it to seconds
datetime_col_in_seconds = pd.to_timedelta(datatime_col.loc[0]).dt.total_seconds()
Then you can re-append the converted column to your original data and/or do whatever processing you want.
Eventually, you can convert it back to datetime.
datetime_col = pd.to_datetime(datetime_col_in_seconds, unit='s')
if you have more than one column with dtype object you might have to do some more pre-processing but I guess this is a good way to start tackling your particular case.
This does what I need
for column_name in df.columns:
try:
df.loc[:, column_name] = pd.to_timedelta(df.loc[:, column_name].astype(str))
except ValueError:
pass
This tries to convert every column into a timedelta format. If it isn't capable of transforming it, it returns a value error and moves onto the next column.
After being run any columns that could be recognized as a timedelta format are transformed.