Looking to generate a number for the days in business days between current date and the end of the month of a pandas dataframe.
E.g. 26/06/2017 - 4, 23/06/2017 - 5
I'm having trouble as I keep getting a Type Error:
TypeError: Cannot convert input to Timestamp
From line:
result['bdaterange'] = pd.bdate_range(pd.to_datetime(result['dte'], unit='ns').values, pd.to_datetime(result['bdate'], unit='ns').values)
I have a Data Frame result with the column dte in a date format and I'm trying to create a new column (bdaterange) as a simple integer/float that I can use to see how far from month end in business days it has.
Sample data:
bid ask spread dte day bdate
01:49:00 2.17 3.83 1.66 2016-12-20 20.858333 2016-12-30
02:38:00 2.2 3.8 1.60 2016-12-20 20.716667 2016-12-30
22:15:00 2.63 3.12 0.49 2016-12-20 21.166667 2016-12-30
03:16:00 1.63 2.38 0.75 2016-12-21 21.391667 2016-12-30
07:11:00 1.46 2.54 1.08 2016-12-21 21.475000 2016-12-30
I've tried BDay() and using that the day cannot be 6 & 7 in the calculation but have not got anywhere. I came across bdate_range which I believe will be exactly what I'm looking for, but the closest I've got gives me the error Cannot convert input to Timestamp.
My attempt is:
result['bdate'] = pd.to_datetime(result['dte']) + BMonthEnd(0)
result['bdaterange'] = pd.bdate_range(pd.to_datetime(result['dte'], unit='ns').values, pd.to_datetime(result['bdate'], unit='ns').values)
print(result['bdaterange'])
Not sure how to solve the error though.
I think you need length of bdate_range for each row, so need custom function with apply:
#convert only once to datetime
result['dte'] = pd.to_datetime(result['dte'])
f = lambda x: len(pd.bdate_range(x['dte'], x['dte'] + pd.offsets.BMonthEnd(0)))
result['bdaterange'] = result.apply(f, axis=1)
print (result)
bid ask spread dte day bdaterange
01:49:00 2.17 3.83 1.66 2016-12-20 20.858333 9
02:38:00 2.20 3.80 1.60 2016-12-20 20.716667 9
22:15:00 2.63 3.12 0.49 2016-12-20 21.166667 9
03:16:00 1.63 2.38 0.75 2016-12-21 21.391667 8
07:11:00 1.46 2.54 1.08 2016-12-21 21.475000 8
Related
So I want to show this data in just two columns. For example, I want to turn this data
Year Jan Feb Mar Apr May Jun
1997 3.45 2.15 1.89 2.03 2.25 2.20
1998 2.09 2.23 2.24 2.43 2.14 2.17
1999 1.85 1.77 1.79 2.15 2.26 2.30
2000 2.42 2.66 2.79 3.04 3.59 4.29
into this
Date Price
Jan-1977 3.45
Feb-1977 2.15
Mar-1977 1.89
Apr-1977 2.03
....
Jan-2000 2.42
Feb-2000 2.66
So far, I have read about how to combine two columns into another dataframe using .apply() .agg(), but no info how to combine them as I showed above.
import pandas as pd
df = pd.read_csv('matrix-A.csv', index_col =0 )
matrix_b = ({})
new = pd.DataFrame(matrix_b)
new["Date"] = df['Year'].astype(float) + "-" + df["Dec"]
print(new)
I have tried this way, but it of course does not work. I have also tried using pd.Series() but no success
I want to ask whether there is any site where I can learn how to do this, or does anybody know correct way to solve this?
Another possible solution, which is based on pandas.DataFrame.stack:
out = df.set_index('Year').stack()
out.index = ['{}_{}'.format(j, i) for i, j in out.index]
out = out.reset_index()
out.columns = ['Date', 'Value']
Output:
Date Value
0 Jan_1997 3.45
1 Feb_1997 2.15
2 Mar_1997 1.89
3 Apr_1997 2.03
4 May_1997 2.25
....
19 Feb_2000 2.66
20 Mar_2000 2.79
21 Apr_2000 3.04
22 May_2000 3.59
23 Jun_2000 4.29
You can first convert it to long-form using melt. Then, create a new column for Date by combining two columns.
long_df = pd.melt(df, id_vars=['Year'], var_name='Month', value_name="Price")
long_df['Date'] = long_df['Month'] + "-" + long_df['Year'].astype('str')
long_df[['Date', 'Price']]
If you want to sort your date column, here is a good resource. Follow those instructions after melting and before creating the Date column.
You can use pandas.DataFrame.melt :
out = (
df
.melt(id_vars="Year", var_name="Month", value_name="Price")
.assign(month_num= lambda x: pd.to_datetime(x["Month"] , format="%b").dt.month)
.sort_values(by=["Year", "month_num"])
.assign(Date= lambda x: x.pop("Month") + "-" + x.pop("Year").astype(str))
.loc[:, ["Date", "Price"]]
)
# Output :
print(out)
Date Price
0 Jan-1997 3.45
4 Feb-1997 2.15
8 Mar-1997 1.89
12 Apr-1997 2.03
16 May-1997 2.25
.. ... ...
7 Feb-2000 2.66
11 Mar-2000 2.79
15 Apr-2000 3.04
19 May-2000 3.59
23 Jun-2000 4.29
[24 rows x 2 columns]
I have a panda dataframe with the following columns:
Stock ROC5 ROC20 ROC63 ROCmean
0 IBGL.SW -0.59 3.55 6.57 3.18
0 EHYA.SW 0.98 4.00 6.98 3.99
0 HIGH.SW 0.94 4.22 7.18 4.11
0 IHYG.SW 0.56 2.46 6.16 3.06
0 HYGU.SW 1.12 4.56 7.82 4.50
0 IBCI.SW 0.64 3.57 6.04 3.42
0 IAEX.SW 8.34 18.49 14.95 13.93
0 AGED.SW 9.45 24.74 28.13 20.77
0 ISAG.SW 7.97 21.61 34.34 21.31
0 IAPD.SW 0.51 6.62 19.54 8.89
0 IASP.SW 1.08 2.54 12.18 5.27
0 RBOT.SW 10.35 30.53 39.15 26.68
0 RBOD.SW 11.33 30.50 39.69 27.17
0 BRIC.SW 7.24 11.08 75.60 31.31
0 CNYB.SW 1.14 4.78 8.36 4.76
0 FXC.SW 5.68 13.84 19.29 12.94
0 DJSXE.SW 3.11 9.24 6.44 6.26
0 CSSX5E.SW -0.53 5.29 11.85 5.54
How can I write in the dataframe a new columns "Symbol" with the stock without ".SW".
Example first row result should be IBGL (modified value IBGL.SW).
Example last row result should be CSSX5E (splited value SSX5E.SW).
If I send the following command:
new_df['Symbol'] = new_df.loc[:, ('Stock')].str.split('.').str[0]
Than I receive an error message:
:3: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
new_df['Symbol'] = new_df.loc[:, ('Stock')].str.split('.').str[0]
How can I solve this problem?
Thanks a lot for your support.
METHOD 1:
You can do a vectorized operation by str.get(0) -
df['SYMBOL'] = df['Stock'].str.split('.').str.get(0)
METHOD 2:
You can do another vectorized operation by using expand=True in str.split() and then getting the first column.
df['SYMBOL'] = df['Stock'].str.split('.', expand = True)[0]
METHOD 3:
Or you can write a custom lambda function with apply (for more complex processes). Note, this is slower but good if you have your own UDF.
df['SYMBOL'] = df['Stock'].apply(lambda x:x.split('.')[0])
This is not an error, but a warning as you may have probably noticed your script finishes its execution.
edite: Given your comments it seems your issues generate previously in the code, therefore I suggest you use the following:
new_df = new_df.copy(deep=False)
And then proceed to solve it with:
new_df.loc['Symbol'] = new_df['Stock'].str.split('.').str[0]
new_df = new_df.copy()
new_df['Symbol'] = new_df.Stock.str.replace('.SW','')
I have a dataframe like this:
Code Date Open High Low Close Volume VWAP TWAP
0 US_GWA_BTC 2014-04-01 467.28 488.62 467.28 479.56 74,776.48 482.76 482.82
1 GWA_BTC 2014-04-02 479.20 494.30 431.32 437.08 114,052.96 460.19 465.93
2 GWA_BTC 2014-04-03 437.33 449.74 414.41 445.60 91,415.08 432.29 433.28
.
316 MWA_XRP_US 2018-01-19 1.57 1.69 1.48 1.53 242,563,870.44 1.59 1.59
317 MWA_XRP_US 2018-01-20 1.54 1.62 1.49 1.57 140,459,727.30 1.56 1.56
I want to filter out rows where code which has GWA infront of it.
I tried this code but it's not working.
df.set_index("Code").filter(regex='[GWA_]*', axis=0)
Try using startswith:
df[df.Code.str.startswith('GWA')]
Hi I have data frame like this:
Ticker P/E P/S P/B P/FCF Dividend
No.
1 NTCT 457.32 3.03 1.44 26.04 -
2 GWRE 416.06 9.80 5.33 45.62 -
3 PEGA 129.02 4.41 9.85 285.10 0.28%
4 BLKB 87.68 4.96 14.36 41.81 0.62%
Firstly, I want to convert values in columns that contain numbers (which are string currently) to a float values. So here I would have the 4 middle columns that need the conversion to float. Would simple loop work with this case?
Second thing, there is a problem with the last column 'Dividend' where there is a percentage value as string. As a matter of fact I can convert it to decimals, however I was thinking if there is a way to still retaining the % and the values would still be calculable.
Any ideas for those two issues?
plan
Take out 'Ticker' because it isn't numeric
use assign to overwrite Dividend by striping off %
use apply with pd.to_numeric to convert all the columns
use eval to get Dividend to proper decimal space
df[['Ticker']].join(
df.assign(
Dividend=df.Dividend.str.strip('%')
).drop('Ticker', 1).apply(
pd.to_numeric, errors='coerce'
)
).eval('Dividend = Dividend / 100', inplace=False)
Ticker P/E P/S P/B P/FCF Dividend
No.
1 NTCT 457.32 3.03 1.44 26.04 NaN
2 GWRE 416.06 9.80 5.33 45.62 NaN
3 PEGA 129.02 4.41 9.85 285.10 0.0028
4 BLKB 87.68 4.96 14.36 41.81 0.0062
more lines
more readable
nums = df.drop('Ticker', 1).assign(Dividend=df.Dividend.str.strip('%'))
nums = nums.apply(pd.to_numeric, errors='coerce')
nums = nums.assign(Dividend=nums.Dividend / 100)
df[['Ticker']].join(nums)
Ticker P/E P/S P/B P/FCF Dividend
No.
1 NTCT 457.32 3.03 1.44 26.04 NaN
2 GWRE 416.06 9.80 5.33 45.62 NaN
3 PEGA 129.02 4.41 9.85 285.10 0.0028
4 BLKB 87.68 4.96 14.36 41.81 0.0062
Assuming that all P/... columns contain proper numbers:
In [47]: df.assign(Dividend=pd.to_numeric(df.Dividend.str.replace(r'\%',''), errors='coerce')
...: .div(100)) \
...: .set_index('Ticker', append=True) \
...: .astype('float') \
...: .reset_index('Ticker')
...:
Out[47]:
Ticker P/E P/S P/B P/FCF Dividend
No.
1 NTCT 457.32 3.03 1.44 26.04 NaN
2 GWRE 416.06 9.80 5.33 45.62 NaN
3 PEGA 129.02 4.41 9.85 285.10 0.0028
4 BLKB 87.68 4.96 14.36 41.81 0.0062
I am brand new to pandas and working with two dataframes. My goal is to append the non-date values of df_ls (below) column-wise to their nearest respective date in df_1. Is the only way to do this with a traditional for-loop or is their some more effective built-in method/function. I have googled this extensively without any luck and have only found ways to append blocks of dataframes to other dataframes. I haven't found a way to search through a dataframe and append a row in another dataframe at the nearest respective date. See example below:
Example of first dataframe (lets call it df_ls):
DATE ALBEDO_SUR B13_RATIO B23_RATIO B1_RAW B2_RAW
0 1999-07-04 0.070771 1.606958 1.292280 0.128069 0.103018
1 1999-07-20 0.030795 2.326290 1.728147 0.099020 0.073595
2 1999-08-21 0.022819 2.492871 1.762536 0.096888 0.068502
3 1999-09-06 0.014613 2.792271 1.894225 0.090590 0.061445
4 1999-10-08 0.004978 2.781847 1.790768 0.089291 0.057521
5 1999-10-24 0.003144 2.818474 1.805257 0.090623 0.058054
6 1999-11-09 0.000859 3.146100 1.993941 0.092787 0.058823
7 1999-12-11 0.000912 2.913604 1.656642 0.097239 0.055357
8 1999-12-27 0.000877 2.974692 1.799949 0.098282 0.059427
9 2000-01-28 0.000758 3.092533 1.782112 0.095153 0.054809
10 2000-03-16 0.002933 2.969185 1.727465 0.083059 0.048322
11 2000-04-01 0.016814 2.366437 1.514110 0.089720 0.057398
12 2000-05-03 0.047370 1.847763 1.401930 0.109767 0.083290
13 2000-05-19 0.089432 1.402798 1.178798 0.137965 0.115936
14 2000-06-04 0.056340 1.807828 1.422489 0.118601 0.093328
Example of second dataframe (let's call it df_1)
Sample Date Value
0 2000-05-09 1.68
1 2000-05-09 1.68
2 2000-05-18 1.75
3 2000-05-18 1.75
4 2000-05-31 1.40
5 2000-05-31 1.40
6 2000-06-13 1.07
7 2000-06-13 1.07
8 2000-06-27 1.49
9 2000-06-27 1.49
10 2000-07-11 2.29
11 2000-07-11 2.29
In the end, my goal is to have something like this (Note the appended values are values closest to the Sample Date, even though they dont match up perfectly):
Sample Date Value ALBEDO_SUR B13_RATIO B23_RATIO B1_RAW B2_RAW
0 2000-05-09 1.68 0.047370 1.847763 1.401930 0.109767 0.083290
1 2000-05-09 1.68 0.047370 1.847763 1.401930 0.109767 0.083290
2 2000-05-18 1.75 0.089432 1.402798 1.178798 0.137965 0.115936
3 2000-05-18 1.75 0.089432 1.402798 1.178798 0.137965 0.115936
4 2000-05-31 1.40 0.056340 1.807828 1.422489 0.118601 0.093328
5 2000-05-31 1.40 0.056340 1.807828 1.422489 0.118601 0.093328
6 2000-06-13 1.07 ETC.... ETC.... ETC ...
7 2000-06-13 1.07
8 2000-06-27 1.49
9 2000-06-27 1.49
10 2000-07-11 2.29
11 2000-07-11 2.29
Thanks for any and all help. As I said I am new to this and I have experience with this sort of thing in MATLAB but PANDAS is a new to me.
Thanks