I have a table like this (with more columns):
date,Sector,Value1,Value2
14/03/22,Medical,86,64
14/03/22,Medical,464,99
14/03/22,Industry,22,35
14/03/22,Services,555,843
15/03/22,Services,111,533
15/03/22,Industry,222,169
15/03/22,Medical,672,937
15/03/22,Medical,5534,825
I have created some features like this:
sectorGroup = df.groupby(["date","Sector"])["Value1","Value2"].mean().reset_index()
df = pd.merge(df,sectorGroup,on=["date","Sector"],how="left",suffixes=["","_bySector"])
dateGroupGroup = df.groupby(["date"])["Value1","Value2"].mean().reset_index()
df = pd.merge(df,dateGroupGroup,on=["date"],how="left",suffixes=["","_byDate"])
Now my new df looks like this:
date,Sector,Value1,Value2,Value1_bySector,Value2_bySector,Value1_byDate,Value2_byDate
14/03/22,Medical,86,64,275.0,81.5,281.75,260.25
14/03/22,Medical,464,99,275.0,81.5,281.75,260.25
14/03/22,Industry,22,35,22.0,35.0,281.75,260.25
14/03/22,Services,555,843,555.0,843.0,281.75,260.25
15/03/22,Services,111,533,111.0,533.0,1634.75,616.0
15/03/22,Industry,222,169,222.0,169.0,1634.75,616.0
15/03/22,Medical,672,937,3103.0,881.0,1634.75,616.0
15/03/22,Medical,5534,825,3103.0,881.0,1634.75,616.0
Now, I want to create lag features for Value1_bySector,Value2_bySector,Value1_byDate,Value2_byDate
For example, a new column named Value1_by_Date_lag1 and Value1_bySector_lag1.
And this new column will look like this:
date,Sector,Value1_by_Date_lag1,Value1_bySector_lag1
15/03/22,Services,281.75,555.0
15/03/22,Industry,281.75,22.0
15/03/22,Medical,281.75,275.0
15/03/22,Medical,281.75,275.0
Basically in Value1_by_Date_lag1, the date "15/03" will contain the value "281.75" which is for the date "14/03" (lag of 1 shift).
Basically in Value1_bySector_lag1, the date "15/03" and Sector "Medical" will contain the value "275.0", which is the value for "14/03" and "Medical" rows.
I hope, the question is clear and gave you all the details.
Create a lagged date variable by shifting the date column, and then merge again with dateGroupGroup and sectorGroup using the lagged date instead of the actual date.
df = pd.read_csv(io.StringIO("""date,Sector,Value1,Value2
14/03/22,Medical,86,64
14/03/22,Medical,464,99
14/03/22,Industry,22,35
14/03/22,Services,555,843
15/03/22,Services,111,533
15/03/22,Industry,222,169
15/03/22,Medical,672,937
15/03/22,Medical,5534,825"""))
# Add a lagged date variable
lagged = df.groupby("date")["date"].first().shift()
df = df.join(lagged, on="date", rsuffix="_lag")
# Create date and sector groups and merge them into df, as you already do
sectorGroup = df.groupby(["date","Sector"])[["Value1","Value2"]].mean().reset_index()
df = pd.merge(df,sectorGroup,on=["date","Sector"],how="left",suffixes=["","_bySector"])
dateGroupGroup = df.groupby("date")[["Value1","Value2"]].mean().reset_index()
df = pd.merge(df, dateGroupGroup, on="date",how="left", suffixes=["","_byDate"])
# Merge again, this time matching the lagged date in df to the actual date in sectorGroup and dateGroupGroup
df = pd.merge(df, sectorGroup, left_on=["date_lag", "Sector"], right_on=["date", "Sector"], how="left", suffixes=["", "_by_sector_lag"])
df = pd.merge(df, dateGroupGroup, left_on="date_lag", right_on="date", how="left", suffixes=["", "_by_date_lag"])
# Drop the extra unnecessary columns that have been created in the merge
df = df.drop(columns=['date_by_date_lag', 'date_by_sector_lag'])
This assumes the data is sorted by date - if not you will have to sort before generating the lagged date. It will work whether or not all the dates are consecutive.
I found 1 inefficient solution (slow and memory intensive).
Lag of "date" group
cols = ["Value1_byDate","Value2_byDate"]
temp = df[["date"]+cols]
temp = temp.drop_duplicates()
for i in range(10):
temp.date = temp.date.shift(-1-i)
df = pd.merge(df,temp,on="date",how="left",suffixes=["","_lag"+str(i+1)])
Lag of "date" and "Sector" group
cols = ["Value1_bySector","Value2_bySector"]
temp = df[["date","Sector"]+cols]
temp = temp.drop_duplicates()
for i in range(10):
temp[["Value1_bySector","Value2_bySector"]] = temp.groupby("Sector")["Value1_bySector","Value2_bySector"].shift(1+1)
df = pd.merge(df,temp,on=["date","Sector"],how="left",suffixes=["","_lag"+str(i+1)])
Is there a more simple solution?
Related
I've joined or concatenated two series into a dataframe. However one of the issues I'm not facing is that I have no column headings on the actual data that would help me do a sort
hist_a = pd.crosstab(category_a, category, normalize=True)
hist_b = pd.crosstab(category_b, category, normalize=True)
counts_a = pd.Series(np.diag(hist_a), index=[hist_a.index])
counts_b = pd.Series(np.diag(hist_b), index=[hist_b.index])
df_plots = pd.concat([counts_a, counts_b], axis=1).fillna(0)
The data looks like the following:
0 1
category
0017817703277 0.000516 5.384341e-04
0017817703284 0.000516 5.384341e-04
0017817731348 0.000216 2.856169e-04
0017817731355 0.000216 2.856169e-04
and I'd like to do a sort, but there are no proper column headings
df_plots = df_plots.sort_values(by=['0?'])
But the dataframe seems to be in two parts. How could I better structure the dataframe to have 'proper' columns such as '0' or 'plot a' rather than being indexable by an integer, which seems to be hard to work with.
category plot a plot b
0017817703277 0.000516 5.384341e-04
0017817703284 0.000516 5.384341e-04
0017817731348 0.000216 2.856169e-04
0017817731355 0.000216 2.856169e-04
Just rename the columns of the dataframe, for example:
df = pd.DataFrame({0:[1,23]})
df = df.rename(columns={0:'new name'})
If you have a lot of columns you rename all of them at once like:
df = pd.DataFrame({0:[1,23]})
rename_dict = {key: f'Col {key}' for key in df.keys() }
df = df.rename(columns=rename_dict)
You can also define the series with the name, so you avoid changing the name afterwards:
counts_a = pd.Series(np.diag(hist_a), index=[hist_a.index], name = 'counts_a')
counts_b = pd.Series(np.diag(hist_b), index=[hist_b.index], name = 'counts_b')
I have a large DataFrame and I am interested in three main columns: individual id, education, and year. I would like to create a new variable called education1985 where I assign to all the individuals the education they had in 1985, no matter what year is in the row. I want to do that without a loop since my data is very large. Also, I want to do it without knowing the different individual's IDs in advance. Here I attach an example with a created data of what I need.
# This is the initial data frame :
individual_id = [1,1,1,1,2,2,2,2,2,4,4,4,4,5,5,6,6,6,7,7,8,9,9,9,9,9,9]
education = [1,1,2,3,1,2,3,3,3,2,2,3,4,3,4,4,4,5,1,2,2,1,2,3,3,3,3]
year = [1984,1985,1986,1987,1983,1984,1985,1986,1987,1985,1986,1987,1989,1984,1985,1984,1985,1986,1985,1986,1985,1983,1984,1985,1986,1987,1987]
df = pd.DataFrame()
df["id"] = individual_id
df["education"] = education
df["year"] = year
# And the desired outcome is create the variable educ85 :
df2 = df.copy()
df2["educ85"] = educ85
Many thanks!!
Try this:
df = df.join(
other=df[df.year==1985][['id','education']].set_index('id').rename(
columns={'education': 'educ85'}
),
how='left',
on='id'
)
I tend to like approaches that are relatively simple.
Initial data setup.
individual_id = [1,1,1,1,2,2,2,2,2,4,4,4,4,5,5,6,6,6,7,7,8,9,9,9,9,9,9]
education = [1,1,2,3,1,2,3,3,3,2,2,3,4,3,4,4,4,5,1,2,2,1,2,3,3,3,3]
year = [1984,1985,1986,1987,1983,1984,1985,1986,1987,1985,1986,1987,1989,1984,1985,1984,1985,1986,1985,1986,1985,1983,1984,1985,1986,1987,1987]
df = pd.DataFrame()
df["id"] = individual_id
df["education"] = education
df["year"] = year
Filter the data down to the 1985 data and then create a dictionary mapping ID to Education.
d1985 = df[df.year == 1985]
d1985 = dict(zip(d1985.id, d1985.education))
Then, create your new DataFrame -
df2 = df.copy()
df2['educ85'] = df2.id.replace(d1985)
Happy 2020! I would like to create a dataframe based on two others. I have the below two dataframes:
df1 = pd.DataFrame({'date':['03.05.1982','04.05.1982','05.05.1982','06.05.1982','07.05.1982','10.05.1982','11.05.1982'],'A': [63.63,64.08,64.19,65.11,65.36,65.25,65.36], 'B': [63.83, 64.10, 64.19, 65.08, 65.33, 65.28, 65.36], 'C':[63.99, 64.22, 64.30, 65.16, 65.41, 65.36, 65.44]})
df2 = pd.DataFrame({'Name':['A','B','C'],'Notice': ['05.05.1982','07.05.1982','12.05.1982']})
The idea is to create df3 such that this dataframe takes the value of A until A's notice date (found in df2) is reached, then df3 switches to the values of B until B's notice date is reached and so on. When we are during notice date, it should take the mean between the current column and the next one.
In the above example, df3 should be as follows (with formulas to illustrate):
df3 = pd.DataFrame({'date':['03.05.1982','04.05.1982','05.05.1982','06.05.1982','07.05.1982','10.05.1982','11.05.1982'], 'Result':[63.63,64.08,(64.19+64.19)/2,65.08,(65.33+65.41)/2,65.36,65.44]})
My idea was to first create a temporary dataframe with same dimensions as df1 and to fill it with 1's when the index date is prior to notice and 0's after. Doing a rolling mean with window 1 would give for each column a series of 1 until I reach 0.5 (signalling a switch).
Not sure if there is a better way to get df3?
I tried the following:
def fill_rule(df_p,df_t):
return np.where(df_p.index > df_t[df_t.Name==df_p.name]['Notice'][0], 0, 1)
df1['date'] = pd.to_datetime(df1['date'])
df2['notice'] = pd.to_datetime(df2['notice'])
df1.set_index("date", inplace = True)
temp = df1.apply(lambda x: fill_rule(x, df2), axis = 0)
And I got the following error: KeyError: (0, 'occurred at index B')
df1['t'] = df1['date'].map(df2.set_index(["Notice"])['Name'])
df1['t'] =df1['t'].fillna(method='bfill').fillna("C")
df3 = pd.DataFrame()
df3['Result'] = df1.apply(lambda row: row[row['t']],axis =1)
df3['date'] = df1['date']
You can use the between method to select the specific date ranges in both dataframes and then use iloc to substitute the specific values
#Initializing the output
df3 = df1.copy()
df3.drop(['B','C'], axis = 1, inplace = True)
df3.columns = ['date','Result']
df3['Result'] = 0.0
df3['count'] = 0
#Modifying df2 to add a dummy sample at the beginning
temp = df2.copy()
temp = temp.iloc[0]
temp = pd.DataFrame(temp).T
temp.Name ='Z'
temp.Notice = pd.to_datetime("05-05-1980")
df2 = pd.concat([temp,df2])
for i in range(len(df2)-1):
startDate = df2.iloc[i]['Notice']
endDate = df2.iloc[i+1]['Notice']
name = df2.iloc[i+1]['Name']
indices = [df1.date.between(startDate, endDate, inclusive=True)][0]
df3.loc[indices,'Result'] += df1[indices][name]
df3.loc[indices,'count'] += 1
df3.Result = df3.apply(lambda x : x.Result/x['count'], axis = 1)
I working with a forex dataset, trying to fill in my dataframe with open, high, low, close updated every tick.
Here is my code:
import pandas as pd
# pandas settings
pd.set_option('display.max_columns', 320)
pd.set_option('display.max_rows', 320)
pd.set_option('display.width', 320)
# creating dataframe
df = pd.read_csv('https://www.dropbox.com/s/tcek3kmleklgxm5/eur_usd_lastweek.csv?dl=1', names=['timestamp', 'ask', 'bid', 'avol', 'bvol'], parse_dates=[0], header=0)
df['spread'] = df.ask - df.bid
df['symbol'] = 'EURUSD'
times = pd.DatetimeIndex(df.timestamp)
# parameters for df.groupby()
df['date'] = times.date
df['hour'] = times.hour
# 1h candles updated every tick
df['candle_number'] = '...'
df['1h_open'] = '...'
df['1h_high'] = '...'
df['1h_low'] = '...'
df['1h_close'] = '...'
# print(df)
grouped = df.groupby(['date', 'hour'])
for idx, x in enumerate(grouped):
print(idx)
print(x)
So as you can see, with for loop I'm getting groups.
Now I want to fill the following columns in my dataframe:
idx be my df['candle_number']
df['1h_open'] must be equal to the very first df.bid in the group
df['1h_high'] = the highest number in df.bid up until current row (so for instance if there are 350 rows in the group, for 20th value
we count the highest number from 0-20 span, on 215th value we the
highest value from 0-215 span which can be completely different.
df['1h_low'] = lowest value up until the current iteration (same approach as for the above)
I hope it's not too confusing =)
Cheers
It's convinient to reindex on date and hour:
df_new = df.set_index(['date', 'hour'])
Then apply groupby functions aggregating by index:
df_new['candle_number'] = df_new.groupby(level=[0,1]).ngroup()
df_new['1h_open'] = df_new.groupby(level=[0,1])['bid'].first()
df_new['1h_high'] = df_new.groupby(level=[0,1])['bid'].cummax()
df_new['1h_low'] = df_new.groupby(level=[0,1])['bid'].cummin()
you can reset_index() back to a flat dataframe.
I am having trouble reformatting a dataframe.
My input is a day value rows by symbols columns (each symbol has different dates with it's values):
Input
code to generate input
data = [("01-01-2010", 15, 10), ("02-01-2010", 16, 11), ("03-01-2010", 16.5, 10.5)]
labels = ["date", "AAPL", "AMZN"]
df_input = pd.DataFrame.from_records(data, columns=labels)
The needed output is (month row with new row for each month):
Needed output
code to generate output
data = [("01-01-2010","29-01-2010", "AAPL", 15, 20), ("01-01-2010","29-01-2010", "AMZN", 10, 15),("02-02-2010","30-02-2010", "AAPL", 20, 32)]
labels = ['bd start month', 'bd end month','stock', 'start_month_value', "end_month_value"]
df = pd.DataFrame.from_records(data, columns=labels)
Meaning (Pseudo code)
1. for each row take only non nan values to create a new "row" (maybe dictionary with the date as the index and the [stock, value] as the value.
2. take only rows that are business start of month or business end of month.
3. write those rows to a new datatframe.
I have read several posts like this and this and several more.
All treat with dataframe of the same "type" and just resampling while I need to change to structure...
My code so far
# creating the new index with business days
df1 =pd.DataFrame(range(10000), index = pd.date_range(df.iloc[0].name, periods=10000, freq='D'))
from pandas.tseries.offsets import CustomBusinessMonthBegin
from pandas.tseries.holiday import USFederalHolidayCalendar
bmth_us = CustomBusinessMonthBegin(calendar=USFederalHolidayCalendar())
df2 = df1.resample(bmth_us).mean()
# creating the new index interseting my old one (daily) with the monthly index
new_index = df.index.intersection(df2.index)
# selecting only the rows I want
df = df.loc[new_index]
# creating a dict that will be my new dataset
new_dict = collections.OrderedDict()
# iterating over the rows and adding to dictionary
for index, row in df.iterrows():
# print index
date = df.loc[index].name
# values are the not none values
values = df.loc[index][~df.loc[index].isnull().values]
new_dict[date]=values
# from dict to list
data=[]
for key, values in new_dict.iteritems():
for i in range(0, len(values)):
date = key
stock_name = str(values.index[i])
stock_value = values.iloc[i]
row = (key, stock_name, stock_value)
data.append(row)
# from the list to df
labels = ['date','stock', 'value']
df = pd.DataFrame.from_records(data, columns=labels)
df.to_excel("migdal_format.xls")
Current output I get
One big problem:
I only get value of the stock on the start of month day.. I need start and end so I can calculate the stock gain on this month..
One smaller problem:
I am sure this is not the cleanest and fastest code :)
Thanks a lot!
So I have found a way.
looping through each column
groupby month
taking the first and last value I have in that month
calculate return
df_migdal = pd.DataFrame()
for col in df_input.columns[0:]:
stock_position = df_input.loc[:,col]
name = stock_position.name
name = re.sub('[^a-zA-Z]+', '', name)
name = name[0:-4]
stock_position=stock_position.groupby([pd.TimeGrouper('M')]).agg(['first', 'last'])
stock_position["name"] = name
stock_position["return"] = ((stock_position["last"] / stock_position["first"]) - 1) * 100
stock_position.dropna(inplace=True)
df_migdal=df_migdal.append(stock_position)
df_migdal=df_migdal.round(decimals=2)
I tried I way cooler way, but did not know how to handle the ,multi index I got... I needed that for each column, to take the two sub columns and create a third one from some lambda function.
df_input.groupby([pd.TimeGrouper('M')]).agg(['first', 'last'])