I'm hoping that there is a relatively simple solution to my problem:
I have a csv with a selection of data points but they all include a date field.
I would like to be able to split up the csv into multiple files based on the month of the date field.
For example: I would like to be able to have all the records before March 2015 in 1 file, all before April 2015 in another, up to all before October 2016 etc.
In this case there will be many duplicate records between the files.
Is there a way to do this with a simple bit of python code or is there an easier method?
Thanks in advance
This code assumes that the date field is in the first column and is labelled "dates". We use pandas to read in the data to a dataframe and pass ['dates'] as the column to convert to date objects. We then take different slices of the dataframe using the year and month to create subsetted views. Each view is then dumped to a new csv with the format year_month.csv
import pandas as pd
df = pd.read_csv('filename.csv', parse_dates=['dates'])
for year in df.dates.apply(lambda x: x.year).unique():
for month in df.dates.apply(lambda x: x.month).unique():
view = df[df.dates.apply(lambda x: x.month == month and x.year==year)]
if view.size:
view.to_csv('{}_{:0>2}.csv'.format(year, month))
There is probably a better way to do this, but this will get the job done.
Updating this to include date_parser to solve the attribute error: 'str' object has no attribute 'year' issue.
import pandas as pd
df = pd.read_csv('filename.csv', parse_dates=['Date'], date_parser=pd.to_datetime)
for year in df['Date'].apply(lambda x: x.year).unique():
for month in df['Date'].apply(lambda x: x.month).unique():
view = df[df['Date'].apply(lambda x: x.month == month and x.year == year)]
if view.size:
view.to_csv('{}_{:0>2}.csv'.format(year, month), index=False)
Related
Dataset
Hi, I Have a Index ['release_date'] in a format of month,date,year , I was trying to split this column by doing
test['date_added'].str.split(' ',expand=True) #code_1
but now it's creating a 4 columns and what really is happening is for some reason is it is simply for few rows it's shifting columns therefore creating a 4th column
code_1
This is the error I am facing
I tried splitting ['release_date'], I am expecting it to be splitted into 3 rows but for some reason few rows are being shifting to other column.
if someone wants to inspect that dataframe you can use google colab for it,
!gdown 1x-_Kq9qYrybB9-DxJHoeVlPabmAm6xbQ
you can use:
df['day'] = pd.DatetimeIndex(df['date_added']).day
df['Month'] = pd.DatetimeIndex(df['date_added']).month
df['year'] = pd.DatetimeIndex(df['date_added']).year
day, month, year = zip(*[(d.day, d.month, d.year) for d in df['date_added']])
df df.assign(day = day, month = month, year = year)
1. Question
I have a dataframe, and the Year-Month column contains the year and month which I want to extract.
For example, an element in this column is "2022-10". And I want to extract year=2022, month=10 from it.
My current solution is to use apply and lambda function:
df['xx_month'] = df['Year-Month'].apply(lambda x: int(x.split('-')[1]))
But it's super slow on a huge dataframe.
How to do it more efficiently?
2. Solutions
Thanks for your wisdom, I summarized each one's solution with the code:
(1) split by '-' and join #Vitalizzare
pandas.Series.str.split - split strings of a series, if expand=True then return a data frame with each part in a separate column;
pandas.DataFrame.set_axis - if axis='columns' then rename column names of a data frame;
pandas.DataFrame.join - if the indices are equal, then the frames stacked together horizontally are returned.
df = pd.DataFrame({'Year-Month':['2022-10','2022-11','2022-12']})
df = df.join(
df['Year-Month']
.str.split('-', expand=True)
.set_axis(['year','month'], axis='columns')
)
(2) convert the datatype from object (str) into datetime format #Neele22
import pandas as pd
df['Year-Month'] = pd.to_datetime(df['Year-Month'], format="%Y-%m")
(3) use regex or datetime to extract year and month #mozway
df['Year-Month'].str.extract(r'(?P<year>\d+)-(?P<month>\d+)').astype(int)
# If you want to assign the output to the same DataFrame while removing the original Year-Month:
df[['year', 'month']] = df.pop('Year-Month').str.extract(r'(\d+)-(\d+)').astype(int)
Or use datetime:
date = pd.to_datetime(df['Year-Month'])
df['year'] = date.dt.year
df['month'] = date.dt.month
3. Follow up question
But there will be a problem if I want to subtract 'Year-Month' with other datetime columns after converting the incomplete 'Year-Month' column from string to datetime.
For example, if I want to get the data which is no later than 2 months after the timestamp of each record.
import dateutil # dateutil is a better package than datetime package according to my experience
df[(df['timestamp'] - df['Year-Month'])>= dateutil.relativedelta.relativedelta(months=0) and (df['timestamp'] - df['Year-Month'])<= datetime.timedelta(months=2)]
This code will have type error for subtracting the converted Year-Month column with actual datetime column.
TypeError: Cannot subtract tz-naive and tz-aware datetime-like objects
The types for these two columns are:
Year-Month is datetime64[ns]
timestamp is datetime64[ns, UTC]
Then, I tried to specify utc=True when changing Year-Month to datetime type:
df[["Year-Month"]] = pd.to_datetime(df[["Year-Month"]],utc=True,format="%Y-%m")
But I got Value Error.
ValueError: to assemble mappings requires at least that [year, month,
day] be specified: [day,month,year] is missing
4. Take away
If the [day,month,year] is not complete for the elements in a column. (like in my case, I only have year and month), we can't change this column from string type into datetime type to do calculations. But to use the extracted day and month to do the calculations.
If you don't need to do calculations between the incomplete datetime column and other datetime columns like me, you can change the incomplete datetime string into datetime type, and extract [day,month,year] from it. It's easier than using regex, split and join.
df = pd.DataFrame({'Year-Month':['2022-10','2022-11','2022-12']})
df = df.join(
df['Year-Month']
.str.split('-', expand=True)
.set_axis(['year','month'], axis='columns')
)
pandas.Series.str.split - split strings of a series, if expand=True then return a data frame with each part in a separate column;
pandas.DataFrame.set_axis - if axis='columns' then rename column names of a data frame;
pandas.DataFrame.join - if the indices are equal, then the frames stacked together horizontally are returned.
You can use a regex for that.
Creating a new DataFrame:
df['Year-Month'].str.extract(r'(?P<year>\d+)-(?P<month>\d+)').astype(int)
If you want to assign the output to the same DataFrame while removing the original Year-Month:
df[['year', 'month']] = df.pop('Year-Month').str.extract(r'(\d+)-(\d+)').astype(int)
Example input:
Year-Month
0 2022-10
output:
year month
0 2022 10
alternative using datetime:
You can also use a datetime intermediate
date = pd.to_datetime(df['Year-Month'])
df['year'] = date.dt.year
df['month'] = date.dt.month
output:
Year-Month year month
0 2022-10 2022 10
You can also convert the datatype from object (str) into datetime format. This will make it easier to work with the dates.
import pandas as pd
df['Year-Month'] = pd.to_datetime(df['Year-Month'], format="%Y-%m")
I have a dataframe that I want to groupby year and followed by months within each year. Due to the fact that the data are quite huge (recorded from 3 decades ago till now), I would like to have the output presented as shown below for subsequent calculation but without any aggregate function such ".mean()" behind.
However, I am unable to do so because groupby always require an .agg, else it will show this error: <pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000022BF79A52E0>
On the other hand, I am a bit worried about importing as Series because I do not know how to set the parameters to get exactly the same format as below. Another reason is that I used the below lines to import the .csv into dataframe:
df=pd.read_csv(r'file directory', index_col = 'date')
df.index = pd.to_datetime(df.index)
For some weird reasons, if I define the date string format in pd.read_csv to import and subsequently, try to sort by other methods according to years and month, function or method that gets confused when the records have date starts off with 01(day)/01(month)/1990 and 01(day)/02(month)/1990. For example, it interprets the first number in Jan as day and the second number as month and sorts all chronologically but when it comes to Feb, when the day is should be 01, the method thought that 01 is the month and 02 is the day portion and move that Feb record to the Jan group.
Are there any ways to achieve the same format?
Methods shown in the post below does not seem to help me get the format I want: Pandas - Groupby dataframe store as dataframe without aggregating
IIUC:
You can use dayfirst parameter in to_datetime() and set that equal to True then create 'Year' and 'Month' column and make them index and sort index:
df=pd.read_csv(r'file directory')
df['date']=pd.to_datetime(df['date'],dayfirst=True)
df['Year']=df['date'].dt.year
df['Month']=df['date'].dt.month
df=df.set_index(['Year','Month']).sort_index()
OR in 3 steps via assign():
df=pd.read_csv(r'file directory')
df['date']=pd.to_datetime(df['date'],dayfirst=True)
df=(df.assign(Year=df['date'].dt.year,Month=df['date'].dt.month)
.set_index(['Year','Month']).sort_index())
you can iterate through the groups of the groupby results.
import pandas as pd
import numpy as np
rand = np.random.RandomState(1)
df = pd.DataFrame({'A': ['foo', 'bar'] * 3,
'B': rand.randn(6),
'C': rand.randint(0, 20, 6)})
groupby_obj = df.groupby(['A'])
for k, gdf in groupby_obj:
print('Groupby Key:', k)
print('Dataframe:\n', gdf, '\n')
you can apply all dataframe methods on gdf
I have a dataframe with columns: customerId, amount, date the date range of this dataframe is: date: 1/1/2016 9/9/2017 I am trying to find the top 10,000 customers will be determined by the total amount of money they have spent in the year 2016; I was going to sort the amount column in descending order and then parse the date column by just 2016 using
mask = (df['date'] >= '1/1/2016') & (df['date'] <'1/1/2017')
there has to be a smarter way to do this, I am new to coding so any help would be appreciated thanks!
Maybe you can try converting the column to datetime by:
df['date'] = pd.to_datetime(df['date'])
#then filter by year
mask = df['date'].apply(lambda x: x.year == 2016)
#A-Za-z's answer is more concise, but in case the column wasn't in datetime type already, you can convert it with pd.to_datetime.
You can use .dt accessor given that the date column is pandas datetime. Otherwise convert it to datetime first
df.date = pd.to_datetime(df.date)
df[df.date.dt.year == 2016]
Should give you the required rows. If you can post the sample dataset, it would be easier to test it
I have a 'myfile.csv' file which has a 'timestamp' column which starts at
(01/05/2015 11:51:00)
and finishes at
(07/05/2015 23:22:00)
A total span of 9,727 minutes
'myfile.csv' also has a column named 'A' which is some numerical value, there are values are multiple values for 'A' within each minute, each with a unique timestamp to the nearest second.
I have code as follows
df = pd.read_csv('myfile.csv')
df = df.set_index('timestamp')
df.index = df.index.to_datetime()
df.sort_index(inplace=True)
df = df['A'].resample('1Min').mean()
df.index = (df.index.map(lambda t: t.strftime('%Y-%m-%d %H:%M')))
My problem is that python seems to think 'timestamp' starts at
(01/05/2015 11:51:00)
-> 5th January
and finishes at
(07/05/2015 23:22:00)
-> 5th July
But really 'timestamp' starts at the
1st May
and finishes at the
7th of May
So the above code produces a dataframe with 261,332 rows, OMG, when it should really only have 9,727 rows.
Somehow Python is mixing up the month with the day, misinterpreting the dates, how do I sort this out?
There are many arguments within csv_read that can help you parse dates from a csv straight into your pandas DataFrame. Here we can set parse_dates with the columns you want as dates and then use dayfirst. This is defaulted to false so the following should do what you want, assuming the dates are in the first column.
df = pd.read_csv('myfile.csv', parse_dates=[0], dayfirst=True)
If the dates column is not the first row, just change the 0 to the column number.
The format of dates that you have included in your question don't seem to match your strftime filter. Take a look at this to fix your string parameter.
It looks to me that it should be something in the lines of:
'%d/%m/%Y %H:%M:%S'