Trying to figure out what I'm doing wrong, new to python and not sure how to solve my AssertionError. If the date in column "sa_death_date" is less than or =to 2020, column "Final Abstraction? Change to 2 if yes." needs to be changed to 2, is greater than 2021, should be 0
Thoughts?
import pandas as pd
import datetime
df=pd.read_csv("C:\\Users\\nm236211\\Desktop\OctoberConsented.DI.REDCap_API.csv")
df.dropna(subset = ["sa_death_date"], inplace=True)
df["sa_death_date"] = pd.to_datetime(df['sa_death_date'])
df.sa_death_date.apply(str)
df.loc[df["sa_death_date"]<= pd.to_datetime(2020,1,1),"Final Abstraction? Change to 2 if yes."] = 2
df.loc[df["sa_death_date"]>= pd.to_datetime(2020,12,31),"Final Abstraction? Change to 2 if yes."] = 0
print(df)
Change the two places of pd.to_datetime(y,m,d) to
pd.datetime(y,m,d)
which is the correct datetime object constructor.
Related
I trying to slice according to a date column (which is calculated based on the index), and only cumulative summing based on the Start Date beside it.
Here is a small sample code to copy/run:
import numpy
import pandas
data = pandas.DataFrame(
{"Bought" : [1,3,4,6]}, index=pandas.to_datetime(['01-01-2020','02-01-2020','03-01-2020','04-01-2020']))
data['StartDate'] = data.index
data['Cum bought2'] = data.loc[data['StartDate']:]['Bought'].cumsum()
It gives me the error "cannot do slice indexing on DatetimeIndex with these indexers".
If I change the data.loc[data['StartDate']:] to a set value (i.e. '02-01-2020'), then it works fine. But I want the start date to be variable and taken from another column.
Edit1: new example. This is close, but the 3rd row shouldn't calculate a value since the Start Date hasn't been reached yet.
import numpy
import pandas
data = pandas.DataFrame(
{"Bought" : [1,3,4,6]}, index=pandas.to_datetime(['01-01-2020','02-01-2020','03-01-2020','04-01-2020']))
data['StartDate'] = ['02-01-2020','02-01-2020','04-01-2020','04-01-2020']
data['Cum Bought'] = data.loc[data['StartDate'].iloc[0]:]['Bought'].cumsum()
Edit2: Also, any idea how to resolve if there are pandas.NaT in the Start Date? I don't want to delete those rows completely, just treat them as zero in calculations.
import numpy
import pandas
data = pandas.DataFrame(
{"Bought" : [1,3,4,6]}, index=pandas.to_datetime(['01-01-2020','02-01-2020','03-01-2020','04-01-2020']))
data['StartDate'] = [pandas.NaT,'02-01-2020','04-01-2020','04-01-2020']
data['Cum Bought'] = data.loc[data['StartDate'].iloc[0]:]['Bought'].cumsum()
You're trying to index with a Series as bound of a slice, which doesn't make sense. You need one value. data.loc[data['StartDate'].iloc[0]:] or data.loc[data['StartDate'].min():] would work.
In your case, you should probably just use:
data['Cum bought2'] = data['Bought'].cumsum()
Or if you're not sure that the dates are sorted:
data['Cum bought2'] = data['Bought'].sort_index().cumsum()
Output:
Bought StartDate Cum bought2
2020-01-01 1 2020-01-01 1
2020-02-01 3 2020-02-01 4
2020-03-01 4 2020-03-01 8
2020-04-01 6 2020-04-01 14
Automating small business reporting from my Quickbooks P&L. I'm trying to get the net income value for the current month from a specific cell in a dataframe, but that cell moves one column to the right every month when I update the csv file.
For example, for the code below, this month I want the value from Nov[0], but next month I'll want the value from Dec[0], even though that column doesn't exist yet.
Is there a graceful way to always select the second right most column, or is this a stupid way to try and get this information?
import numpy as np
import pandas as pd
nov = -810
dec = 14958
total = 8693
d = {'Jan': [50], 'Feb': [70], 'Total':[120]}
df = pd.DataFrame(data=d)
Sure, you can reference the last or second-to-last row or column.
d = {'Jan': [50], 'Feb': [70], 'Total':[120]}
df = pd.DataFrame(data=d)
x = df.iloc[-1,-2]
This will select the value in the last row for the second-to-last column, in this case 70. :)
If you plan to use the full file, #VincentRupp's answer will get you what you want.
But if you only plan to use the values in the second right most column and you can infer what it will be called, you can tell pd.read_csv that's all you want.
import pandas as pd # 1.5.1
# assuming we want this month's name
# can modify to use some other month
abbreviated_month_name = pd.to_datetime("today").strftime("%b")
df = pd.read_csv("path/to/file.csv", usecols=[abbreviated_month_name])
print(df.iloc[-1, 0])
References
pd.read_csv
strftime cheat-sheet
I have a dataframe region_cumulative_df_sel as below:
Month-Day regions RAIN_PERCENTILE_25 RAIN_PERCENTILE_50 RAIN_PERCENTILE_75 RAIN_MEAN RAIN_MEDIAN
07-01 1 0.0611691028 0.2811064720 1.9487996101 1.4330813885 0.2873695195
07-02 1 0.0945720226 0.8130480051 4.5959815979 2.9420840740 1.0614821911
07-03 1 0.2845511734 1.1912839413 5.5803232193 3.7756001949 1.1988518238
07-04 1 0.3402922750 3.2274529934 7.4262523651 5.2195668221 3.2781836987
07-05 1 0.4680584669 5.2418060303 8.6639881134 6.9092760086 5.3968687057
07-06 1 2.4329853058 7.3453550339 10.8091869354 8.7898645401 7.5020875931
... ...
... ...
... ...
06-27 1 382.7809448242 440.1162109375 512.6233520508 466.4956665039 445.0971069336
06-28 1 383.8329162598 446.2222900391 513.2116699219 467.9851379395 451.1973266602
06-29 1 385.7786254883 449.5384826660 513.4027099609 469.5671691895 451.2281188965
06-30 1 386.7952270508 450.6524658203 514.0201416016 471.2863159180 451.2484741211
The index "Month-Day" is a type of String indicating the first day and the last day of a calendar year instead of type of datetime.
I need to use hvplot to develop an interactive plot.
region_cumulative_df_sel.hvplot(width=900)
It is hard to view the labels on the x axis. How can change the xticks to show only 1st of each month, e.g. "07-01", "08-01", "09-01", ... ..., "06-01"?
I tried #Redox code as below:
region_cumulative_df_sel['Month-Day'] = pd.to_datetime(region_cumulative_df_sel['Month-Day'],format="%m-%d") ##Convert to datetime
from bokeh.models.formatters import DatetimeTickFormatter
## Set format for showing x-axis ... you only need days, but in case counts change
formatter = DatetimeTickFormatter(days=["%m-%d"], months=["%m-%d"], years=["%m-%d"])
region_cumulative_df_sel.plot(x='Month-Day', xformatter=formatter, y=['RAIN_PERCENTILE_25','RAIN_PERCENTILE_50','RAIN_PERCENTILE_75','RAIN_MEAN','RAIN_MEDIAN'], width=900, ylabel="Rainfall (mm)",
rot=90, title="Cumulative Rainfall")
This is what I have generated.
How can I shift the xticks on the x-axis to align with the Month-Day values. Also the popup window shows "1900" as year for Month-Day column. Can the year segment be removed?
The x-axis data is in string format. So, holoviews thinks this is categorical and plotting every row. You need to convert it to datetime and this will allow the plotting to be in the format you need. I am taking a simple example and showing how to do this... should work in your case as well...
##My month-day column is string - 07-01 07-02 07-03 07-04 ... 12-31
df['Month-Day']=pd.to_datetime(df['Month-Day'],format="%m-%d") ##Convert to datetime
df['myY']=np.random.randint(100, size=(len(df))) ##Random Y data
from bokeh.models.formatters import DatetimeTickFormatter
## Set format for showing x-axis ... you only need days, but in case counts change
formatter = DatetimeTickFormatter(days=["%m-%d"], months=["%m-%d"], years=["%m-%d"])
##Plot graph
df.plot(x='Month-Day',xformatter=formatter)#.opts(xticks=4, xrotation=90)
#Redox is on the right track here. The issue is with the way the Month-Day column is converted to a datetime; pandas is assuming the year is 1900 for every row.
Essentially you need to attach a year to the Month-Day in some way.
See the example below, this takes the first month-day string, prepends "2022-" and generates sequential daily values for every row (but there are a few ways of doing this).
code:
import pandas as pd
import numpy as np
import hvplot.pandas
from bokeh.models.formatters import DatetimeTickFormatter
dates = pd.date_range("2021-07-01", "2022-06-30", freq="D")
df = pd.DataFrame({
"md": dates.strftime("%m-%d"),
"ign": np.cumsum(np.random.normal(10, 5, len(dates))),
"sup": np.cumsum(np.random.normal(20, 10, len(dates))),
"imp": np.cumsum(np.random.normal(30, 15, len(dates))),
})
df["time"] = pd.date_range("2021-" + df.md[0], periods=len(df.index), freq="D")
formatter = DatetimeTickFormatter(
days=["%m-%d"], months=["%m-%d"], years=["%m-%d"])
df.hvplot(x='time', xformatter=formatter, y=['ign', 'sup', 'imp'],
width=900, ylabel="Index", rot=90, title="Cumulative ISI")
In my project I am trying to create a new column to categorize records by range of hours, let me explain, I have a column in the dataframe called 'TowedTime' with time series data, I want another column to categorize by full hour without minutes, for example if the value in the 'TowedTime' column is 09:32:10 I want it to be categorized as 9 AM, if says 12:45:10 it should be categorized as 12 PM and so on with all the other values. I've read about the .cut and bins function but I can't get the result I want.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import datetime
df = pd.read_excel("Baltimore Towing Division.xlsx",sheet_name="TowingData")
df['Month'] = pd.DatetimeIndex(df['TowedDate']).strftime("%b")
df['Week day'] = pd.DatetimeIndex(df['TowedDate']).strftime("%a")
monthOrder = ['Jan', 'Feb', 'Mar', 'Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
dayOrder = ['Mon','Tue','Wed','Thu','Fri','Sat','Sun']
pivotHours = pd.pivot_table(df, values='TowedDate',index='TowedTime',
columns='Week day',
fill_value=0,
aggfunc= 'count',
margins = False, margins_name='Total').reindex(dayOrder,axis=1)
print(pivotHours)
First, make sure the type of the column 'TowedTime' is datetime. Second, you can easily extract the hour from this data type.
df['TowedTime'] = pd.to_datetime(df['TowedTime'],format='%H:%M:%S')
df['hour'] = df['TowedTime'].dt.hour
hope it answers your question
With the help of #Fabien C I was able to solve the problem.
First, I had to check the data type of values in the 'TowedTime' column with dtypes function. I found that were a Object.
I proceed to try convert 'TowedTime' to datetime:
df['TowedTime'] = pd.to_datetime(df['TowedTime'],format='%H:%M:%S').dt.time
Then to create a new column in the df, for only the hours:
df['Hour'] = pd.to_datetime(df['TowedTime'],format='%H:%M:%S').dt.hour
And the result was this:
You can notice in the image that 'TowedTime' column remains as an object, but the new 'Hour' column correctly returns the hour value.
Originally, the dataset already had the date and time separated into different columns, I think they used some method to separate date and time in excel and this created the time ('TowedTime') to be an object, I could not convert it, Or at least that's what the dtypes function shows me.
I tried all this Pandas methods for converting the Object to Datetime :
df['TowedTime'] = pd.to_datetime(df['TowedTime'])
df['TowedTime'] = pd.to_datetime(df['TowedTime'])
df['TowedTime'] = df['TowedTime'].astype('datetime64[ns]')
df['TowedTime'] = pd.to_datetime(df['TowedTime'], format='%H:%M:%S')
df['TowedTime'] = pd.to_datetime(df['TowedTime'], format='%H:%M:%S')
I have a Pandas DataFrame with a 'date' column. Now I need to filter out all rows in the DataFrame that have dates outside of the next two months. Essentially, I only need to retain the rows that are within the next two months.
What is the best way to achieve this?
If date column is the index, then use .loc for label based indexing or .iloc for positional indexing.
For example:
df.loc['2014-01-01':'2014-02-01']
See details here http://pandas.pydata.org/pandas-docs/stable/dsintro.html#indexing-selection
If the column is not the index you have two choices:
Make it the index (either temporarily or permanently if it's time-series data)
df[(df['date'] > '2013-01-01') & (df['date'] < '2013-02-01')]
See here for the general explanation
Note: .ix is deprecated.
Previous answer is not correct in my experience, you can't pass it a simple string, needs to be a datetime object. So:
import datetime
df.loc[datetime.date(year=2014,month=1,day=1):datetime.date(year=2014,month=2,day=1)]
And if your dates are standardized by importing datetime package, you can simply use:
df[(df['date']>datetime.date(2016,1,1)) & (df['date']<datetime.date(2016,3,1))]
For standarding your date string using datetime package, you can use this function:
import datetime
datetime.datetime.strptime
If you have already converted the string to a date format using pd.to_datetime you can just use:
df = df[(df['Date'] > "2018-01-01") & (df['Date'] < "2019-07-01")]
The shortest way to filter your dataframe by date:
Lets suppose your date column is type of datetime64[ns]
# filter by single day
df_filtered = df[df['date'].dt.strftime('%Y-%m-%d') == '2014-01-01']
# filter by single month
df_filtered = df[df['date'].dt.strftime('%Y-%m') == '2014-01']
# filter by single year
df_filtered = df[df['date'].dt.strftime('%Y') == '2014']
If your datetime column have the Pandas datetime type (e.g. datetime64[ns]), for proper filtering you need the pd.Timestamp object, for example:
from datetime import date
import pandas as pd
value_to_check = pd.Timestamp(date.today().year, 1, 1)
filter_mask = df['date_column'] < value_to_check
filtered_df = df[filter_mask]
If the dates are in the index then simply:
df['20160101':'20160301']
You can use pd.Timestamp to perform a query and a local reference
import pandas as pd
import numpy as np
df = pd.DataFrame()
ts = pd.Timestamp
df['date'] = np.array(np.arange(10) + datetime.now().timestamp(), dtype='M8[s]')
print(df)
print(df.query('date > #ts("20190515T071320")')
with the output
date
0 2019-05-15 07:13:16
1 2019-05-15 07:13:17
2 2019-05-15 07:13:18
3 2019-05-15 07:13:19
4 2019-05-15 07:13:20
5 2019-05-15 07:13:21
6 2019-05-15 07:13:22
7 2019-05-15 07:13:23
8 2019-05-15 07:13:24
9 2019-05-15 07:13:25
date
5 2019-05-15 07:13:21
6 2019-05-15 07:13:22
7 2019-05-15 07:13:23
8 2019-05-15 07:13:24
9 2019-05-15 07:13:25
Have a look at the pandas documentation for DataFrame.query, specifically the mention about the local variabile referenced udsing # prefix. In this case we reference pd.Timestamp using the local alias ts to be able to supply a timestamp string
So when loading the csv data file, we'll need to set the date column as index now as below, in order to filter data based on a range of dates. This was not needed for the now deprecated method: pd.DataFrame.from_csv().
If you just want to show the data for two months from Jan to Feb, e.g. 2020-01-01 to 2020-02-29, you can do so:
import pandas as pd
mydata = pd.read_csv('mydata.csv',index_col='date') # or its index number, e.g. index_col=[0]
mydata['2020-01-01':'2020-02-29'] # will pull all the columns
#if just need one column, e.g. Cost, can be done:
mydata['2020-01-01':'2020-02-29','Cost']
This has been tested working for Python 3.7. Hope you will find this useful.
I'm not allowed to write any comments yet, so I'll write an answer, if somebody will read all of them and reach this one.
If the index of the dataset is a datetime and you want to filter that just by (for example) months, you can do following:
df.loc[df.index.month == 3]
That will filter the dataset for you by March.
How about using pyjanitor
It has cool features.
After pip install pyjanitor
import janitor
df_filtered = df.filter_date(your_date_column_name, start_date, end_date)
You could just select the time range by doing: df.loc['start_date':'end_date']
In pandas version 1.1.3 I encountered a situation where the python datetime based index was in descending order. In this case
df.loc['2021-08-01':'2021-08-31']
returned empty. Whereas
df.loc['2021-08-31':'2021-08-01']
returned the expected data.
Another solution if you would like to use the .query() method.
It allows you to use write readable code like .query(f"{start} < MyDate < {end}") on the trade off, that .query() parses strings and the columns values must be in pandas date format (so that it is also understandable for .query())
df = pd.DataFrame({
'MyValue': [1,2,3],
'MyDate': pd.to_datetime(['2021-01-01','2021-01-02','2021-01-03'])
})
start = datetime.date(2021,1,1).strftime('%Y%m%d')
end = datetime.date(2021,1,3).strftime('%Y%m%d')
df.query(f"{start} < MyDate < {end}")
(following the comment from #Phillip Cloud, answer from #Retozi)
import the pandas library
import pandas as pd
STEP 1: convert the date column into a string using the pd.to_datetime() method
df['date']=pd.to_datetime(df["date"],unit='s')
STEP 2: perform the filtering in any predetermined manner ( i.e 2 months)
df = df[(df["date"] >"2022-03-01" & df["date"] < "2022-05-03")]
STEP 3 : Check the output
print(df)
# 60 days from today
after_60d = pd.to_datetime('today').date() + datetime.timedelta(days=60)
# filter date col less than 60 days date
df[df['date_col'] < after_60d]