I'm trying to read a column with date and time from csv file and wanted to plot the frequencies of datas per day.
I don't actually know how to read them though.
You'll need to define your initial column as datetime first.
df['created'] = pd.to_datetime(df['created'])
Related
I have set Snort up to output alerts into a excel.csv directly with my required information.
I am using Python to input the values in my excel.csv into a database. < This works, no issues here
However one of my values in the excel is the Snort timestamp (MONTH/DAY-HOUR:MIN:SEC.MILIIS).
I wish to separate the date and time into 2 separate columns for me to easily input it into my SQL database.
I am trying to separate the datetime (currently the format is MONTH/DAY-HOUR:MIN:SEC.MILIIS) into Date (DD/MM) and Time (HOUR:MIN:SEC).
Current format in the excel: 04/11-10:47:30.789142
What I would like:
Column 1: 04/11
Column 2: 10:47:30
Current script:
import pandas as pd
import sys
import csv
import datetime
#import my csv
#working, able to read all data
data = pd.read_csv(r'C:\Users\devon\Desktop\testSnort.csv')
print (data)
#Set column "Date Time" in the excel as the variable DateTimeList
#Able to print out the Date+time only
DateTimeList = ["DateTime"]
datetime = pd.read_csv(r'C:\Users\devon\Desktop\testSnort.csv', usecols=DateTimeList)
print (datetime)
I am able to output the current data, and to filter out the DateTime values.
However I do not seem to be able to strip the 2 apart into different columns
Could someone advise me if it is possible?
Thank you!
There are a couple of ways that you can go about it, but if you are reading the timestamp as a string, probably the easiest way to do it is to split on the -.
value1, value2 = timestamp.split("-")
That will give you the month and day in value1 and the time in value2.
date='01/02-12:12:00.0000'
print(date.split('-'))
#['01/02', '12:12:00.0000']
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
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 have an Excel sheet of time series data of prices where each day consists of 6 hourly periods. I am trying to use Python and Pandas which I have setup and working, importing a CSV and then creating a df from this. That is fine it is just the sorting code I am struggling with. In excel I can do this using a Sum(sumifs) array function but I would like it to work in Python/Pandas.
I am looking to produce a new time series from the data where for each day I get the average price for periods 3 to 5 inclusive only, excluding the others. I am struggling with this.
An example raw data exert and result I am looking for is below:
You need filter by between and boolean indexing and then aggregate mean:
df = df[df['Period'].between(3,5)].groupby('Date', as_index=False)['Price'].mean()
Ok I have read some data from a CSV file using:
df=pd.read_csv(path,index_col='Date',parse_dates=True,dayfirst=True)
The data are in European date convention format dd/mm/yyyy, that is why i am using dayfirst=True.
However, what i want to do is change the string format appearance of my dataframe index df from the American(yyyy/mm/dd) to the European format(dd/mm/yyyy) just to visually been consistent with how i am looking the dates.
I could't find any relevant argument in the pd.read_csv method.
In the output I want a dataframe in which simply the index will be a datetime index visually consistent with the European date format.
Could anyone propose a solution? It should be straightforward, since I guess there should be a pandas method to handle that, but i am currently stuck.
Try something like the following once it's loaded from the CSV. I don't believe it's possible to perform the conversion as part of the reading process.
import pandas as pd
df = pd.DataFrame({'date': pd.date_range(start='11/24/2016', periods=4)})
df['date_eu'] = df['date'].dt.strftime('%d/%m/%Y')