I have a excel (.xslx) file with 4 columns:
pmid (int)
gene (string)
disease (string)
label (string)
I attempt to load this directly into python with pandas.read_excel
df = pd.read_excel(path, parse_dates=False)
capture from excel
capture from pandas using my ide debugger
As shown above, pandas tries to be smart, automatically converting some of gene fields such as 3.Oct, 4.Oct to a datetime type. The issue is that 3.Oct or 4.Oct is a abbreviation of Gene type and totally different meaning. so I don't want pandas to do so. How can I prevent pandas from converting types automatically?
Update:
In fact, there is no conversion. The value appears as 2020-10-03 00:00:00 in Pandas because it is the real value stored in the cell. Excel show this value in another format
Update 2:
To keep the same format as Excel, you can use pd.to_datetime and a custom function to reformat the date.
# Sample
>>> df
gene
0 PDGFRA
1 2021-10-03 00:00:00 # Want: 3.Oct
2 2021-10-04 00:00:00 # Want: 4.Oct
>>> df['gene'] = (pd.to_datetime(df['gene'], errors='coerce')
.apply(lambda dt: f"{dt.day}.{calendar.month_abbr[dt.month]}"
if dt is not pd.NaT else np.NaN)
.fillna(df['gene']))
>>> df
gene
0 PDGFRA
1 3.Oct
2 4.Oct
Old answer
Force dtype=str to prevent Pandas try to transform your dataframe
df = pd.read_excel(path, dtype=str)
Or use converters={'colX': str, ...} to map the dtype for each columns.
pd.read_excel has a dtype argument you can use to specify data types explicitly.
I have a dataframe with a column of dates, unfortunately my import (using read_excel) brought in format of dates as datetime and also excel dates as integers.
What I am seeking is a column with dates only in format %Y-%m-%d
From research, excel starts at 1900-01-00, so I could add these integers. I have tried to use str.extract and a regex in order to separate the columns into two, one of datetimes, the other as integers. However the result is NaN.
Here is an input code example
df = pd.DataFrame({'date_from': [pd.Timestamp('2022-09-10 00:00:00'),44476, pd.Timestamp('2021-02-16 00:00:00')], 'date_to': [pd.Timestamp('2022-12-11 00:00:00'),44455, pd.Timestamp('2021-12-16 00:00:00')]})
Attempt to first separate the columns by extracting the integers( dates imported from MS excel)
df.date_from.str.extract(r'(\d\d\d\d\d)')
however this gives NaN.
The reason I have tried to separate integers out of the column, is that I get an error when trying to act on the excel dates within the mixed column (in other words and error using the following code:)
def convert_excel_time(excel_time):
return pd.to_datetime('1900-01-01') + pd.to_timedelta(excel_time,'D')
Any guidance on how I might get a column of dates only? I find the datetime modules and aspects of pandas and python the most frustrating of all to get to grips with!
thanks
You can convert values to timedeltas by to_timedelta with errors='coerce' for NaT if not integers add Timestamp called d, then convert datetimes with errors='coerce' and last pass to Series.fillna in custom function:
def f(x):
#https://stackoverflow.com/a/9574948/2901002
d = pd.Timestamp(1899, 12, 30)
timedeltas = pd.to_timedelta(x, unit='d', errors='coerce')
dates = pd.to_datetime(x, errors='coerce')
return (timedeltas + d).fillna(dates)
cols = ['date_from','date_to']
df[cols] = df[cols].apply(f)
print (df)
date_from date_to
0 2022-09-10 2022-12-11
1 2021-10-07 2021-09-16
2 2021-02-16 2021-12-16
I am trying to convert a column in a pandas dataframe from a string to a timestamp.
Due to a slightly annoying constraint (I am limited by my employers software & IT policy) I am running an older version of Pandas (0.14.1). This version does include the "pd.Timestamp".
Essentially, I want to pass a dataframe column formatted as a string to "pd.Timestamp" to create a column of Timestamps. Here is an example dataframe
'Date/Time String' 'timestamp'
0 2017-01-01 01:02:03 NaN
1 2017-01-02 04:05:06 NaN
2 2017-01-03 07:08:09 NaN
My DataFrame is very big, so iterating through it is really inefficient. But this is what I came up with:
for i in range (len(df['Date/Time String'])):
df['timestamp'].iloc[i] = pd.Timestamp(df['Date/Time String'].iloc[i])
What would be the sensible way to make this operation much faster?
You can check this:
import pandas as pd
df['Date/Time Timestamp'] = pd.to_datetime(df['Date/Time String'])
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]
I'm new to pandas.
I'm like to read the quotes for a number of symbols (e.g. ['SPY', 'IWM', 'QQQ']) from Yahoo (which I do with no problem) and then I'd like to use only the 'Adj Close' columns to build a portfolio of ETFs over a given period of time.
Say that I'd like to start with an empty DataFrame whose index are the dates where the market is open, taken for example from the first df. Subsequently, I'd like to "append" to the right one single column at a time with the 'Adj Close' of each symbol, renamed with the ticker name.
I'm sure it must be simple, but I can't get it. Can anybody help me? thank you in advance.
If you are just using the Adj Close column, it is easiest to extract it immediately after reading the data.
import pandas.io.data as web
df = web.DataReader(['F', 'AAPL', 'IBM'], 'yahoo', '2016-05-02', '2016-05-06')['Adj Close']
>>> df
AAPL F IBM
Date
2016-05-02 93.073328 13.62 143.881476
2016-05-03 94.604009 13.43 142.752373
2016-05-04 93.620002 13.31 142.871221
2016-05-05 93.239998 13.32 145.070003
2016-05-06 92.720001 13.44 147.289993