I have a dataframe,df containing
Index Date & Time eventName eventCount
0 2017-08-09 ABC 24
1 2017-08-09 CDE 140
2 2017-08-10 CDE 150
3 2017-08-11 DEF 200
4 2017-08-11 ABC 20
5 2017-08-16 CDE 10
6 2017-08-16 ABC 15
7 2017-08-17 CDE 10
8 2017-08-17 DEF 50
9 2017-08-18 DEF 80
...
I want to sum the eventCount for each weekly day occurrences and plot for the total events for each weekly day(from MON to SUN) i.e. for example:
Summation of the eventCount values of:
2017-08-09 and 2017-08-16(Mondays)=189
2017-08-10 and 2017-08-17(Tuesdays)=210
2017-08-16 and 2017-08-23(Wednesdays)=300
I have tried
dailyOccurenceSum=df['eventCount'].groupby(lambda x: x.weekday).sum()
and I get this error:AttributeError: 'int' object has no attribute 'weekday'
Starting with df -
df
Index Date & Time eventName eventCount
0 0 2017-08-09 ABC 24
1 1 2017-08-09 CDE 140
2 2 2017-08-10 CDE 150
3 3 2017-08-11 DEF 200
4 4 2017-08-11 ABC 20
5 5 2017-08-16 CDE 10
6 6 2017-08-16 ABC 15
7 7 2017-08-17 CDE 10
8 8 2017-08-17 DEF 50
9 9 2017-08-18 DEF 80
First, convert Date & Time to a datetime column -
df['Date & Time'] = pd.to_datetime(df['Date & Time'])
Next, call groupby + sum on the weekday name.
df = df.groupby(df['Date & Time'].dt.weekday_name)['eventCount'].sum()
df
Date & Time
Friday 300
Thursday 210
Wednesday 189
Name: eventCount, dtype: int64
If you want to sort by weekday, convert the index to categorical and call sort_index -
cat = ['Monday','Tuesday','Wednesday','Thursday','Friday','Saturday', 'Sunday']
df.index = pd.Categorical(df.index, categories=cat, ordered=True)
df = df.sort_index()
df
Wednesday 189
Thursday 210
Friday 300
Name: eventCount, dtype: int64
Related
i have dataframe like this :
trx_date
trx_amount
2013-02-11
35
2014-03-10
26
2011-02-9
10
2013-02-12
5
2013-01-11
21
how do i filter that into month and year? so that i can sum the trx_amount
example expected output :
trx_monthly
trx_sum
2013-02
40
2013-01
21
2014-02
35
You can convert values to month periods by Series.dt.to_period and then aggregate sum:
df['trx_date'] = pd.to_datetime(df['trx_date'])
df1 = (df.groupby(df['trx_date'].dt.to_period('m').rename('trx_monthly'))['trx_amount']
.sum()
.reset_index(name='trx_sum'))
print (df1)
trx_monthly trx_sum
0 2011-02 10
1 2013-01 21
2 2013-02 40
3 2014-03 26
Or convert datetimes to strings in format YYYY-MM by Series.dt.strftime:
df2 = (df.groupby(df['trx_date'].dt.strftime('%Y-%m').rename('trx_monthly'))['trx_amount']
.sum()
.reset_index(name='trx_sum'))
print (df2)
trx_monthly trx_sum
0 2011-02 10
1 2013-01 21
2 2013-02 40
3 2014-03 26
Or convert to month and years, then output is different - 3 columns:
df2 = (df.groupby([df['trx_date'].dt.year.rename('year'),
df['trx_date'].dt.month.rename('month')])['trx_amount']
.sum()
.reset_index(name='trx_sum'))
print (df2)
year month trx_sum
0 2011 2 10
1 2013 1 21
2 2013 2 40
3 2014 3 26
You can try this -
df['trx_month'] = df['trx_date'].dt.month
df_agg = df.groupby('trx_month')['trx_sum'].sum()
Of all the Medals won by these 5 countries across all olympics,
what is the percentage medals won by each one of them?
i have combined all excel file in one using panda dataframe but now stuck with finding percentage
Country Gold Silver Bronze Total
0 USA 10 13 11 34
1 China 2 2 4 8
2 UK 1 0 1 2
3 Germany 12 16 8 36
4 Australia 2 0 0 2
0 USA 9 9 7 25
1 China 2 4 5 11
2 UK 0 1 0 1
3 Germany 11 12 6 29
4 Australia 1 0 1 2
0 USA 9 15 13 37
1 China 5 2 4 11
2 UK 1 0 0 1
3 Germany 10 13 7 30
4 Australia 2 1 0 3
Combined data sheet
Code that i have tried till now
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
df= pd.DataFrame()
for f in ['E:\\olympics\\Olympics-2002.xlsx','E:\\olympics\\Olympics-
2006.xlsx','E:\\olympics\\Olympics-2010.xlsx',
'E:\\olympics\\Olympics-2014.xlsx','E:\\olympics\\Olympics-
2018.xlsx']:
data = pd.read_excel(f,'Sheet1')
df = df.append(data)
df.to_excel("E:\\olympics\\combineddata.xlsx")
data = pd.read_excel("E:\\olympics\\combineddata.xlsx")
print(data)
final_Data={}
for i in data['Country']:
x=i
t1=(data[(data.Country==x)].Total).tolist()
print("Name of Country=",i, int(sum(t1)))
final_Data.update({i:int(sum(t1))})
t3=data.groupby('Country').Total.sum()
t2= df['Total'].sum()
t4= t3/t2*100
print(t3)
print(t2)
print(t4)
this how is got the answer....Now i need to pull that in plot i want to put it pie
Let's assume you have created the DataFrame as 'df'. Then you can do the following to first group by and then calculate percentages.
df = df.groupby('Country').sum()
df['Gold_percent'] = (df['Gold'] / df['Gold'].sum()) * 100
df['Silver_percent'] = (df['Silver'] / df['Silver'].sum()) * 100
df['Bronze_percent'] = (df['Bronze'] / df['Bronze'].sum()) * 100
df['Total_percent'] = (df['Total'] / df['Total'].sum()) * 100
df.round(2)
print (df)
The output will be as follows:
Gold Silver Bronze ... Silver_percent Bronze_percent Total_percent
Country ...
Australia 5 1 1 ... 1.14 1.49 3.02
China 9 8 13 ... 9.09 19.40 12.93
Germany 33 41 21 ... 46.59 31.34 40.95
UK 2 1 1 ... 1.14 1.49 1.72
USA 28 37 31 ... 42.05 46.27 41.38
I am not having the exact dataset what you have . i am explaining with similar dataset .Try to add a column with sum of medals across rows.then find the percentage by dividing all the row by sum of entire column.
i am posting this as model check this
import pandas as pd
cars = {'Brand': ['Honda Civic','Toyota Corolla','Ford Focus','Audi A4'],
'ExshowroomPrice': [21000,26000,28000,34000],'RTOPrice': [2200,250,2700,3500]}
df = pd.DataFrame(cars, columns = ['Brand', 'ExshowroomPrice','RTOPrice'])
Brand ExshowroomPrice RTOPrice
0 Honda Civic 21000 2200
1 Toyota Corolla 26000 250
2 Ford Focus 28000 2700
3 Audi A4 34000 3500
df['percentage']=(df.ExshowroomPrice +df.RTOPrice) * 100
/(df.ExshowroomPrice.sum() +df.RTOPrice.sum())
print(df)
Brand ExshowroomPrice RTOPrice percentage
0 Honda Civic 21000 2200 19.719507
1 Toyota Corolla 26000 250 22.311942
2 Ford Focus 28000 2700 26.094348
3 Audi A4 34000 3500 31.874203
hope its clear
I have a multi indexed dataframe(groupby object) as the result of groupby (by 'id' and 'date').
x y
id date
abc 3/1/1994 100 7
9/1/1994 90 8
3/1/1995 80 9
bka 5/1/1993 50 8
7/1/1993 40 9
I'd like to convert those dates into an integer-like, such as
x y
id date
abc day 0 100 7
day 1 90 8
day 2 80 9
bka day 0 50 8
day 1 40 9
I thought it would be simple but I couldn't get there easily. Is there a simple way to work on this?
Try this:
s = 'day ' + df.groupby(level=0).cumcount().astype(str)
df1 = df.set_index([s], append=True).droplevel(1)
x y
id
abc day 0 100 7
day 1 90 8
day 2 80 9
bka day 0 50 8
day 1 40 9
You can calculate the new level and create a new index:
lvl1 = 'day ' + df.groupby('id').cumcount().astype('str')
df.index = pd.MultiIndex.from_tuples((x,y) for x,y in zip(df.index.get_level_values('id'), lvl1) )
output:
x y
abc day 0 100 7
day 1 90 8
day 2 80 9
bka day 0 50 8
day 1 40 9
I have been successful with converting while working with a different dataset a couple days ago. However, I cannot apply the same technique to my current dataset. The set looks as:
totalHist.columns.values[[0, 1]] = ['Datez', 'Volumez']
totalHist.head()
Datez Volumez
0 2016-09-19 6.300000e+07
1 2016-09-20 3.382694e+07
2 2016-09-26 4.000000e+05
3 2016-09-27 4.900000e+09
4 2016-09-28 5.324995e+08
totalHist.dtypes
Datez object
Volumez float64
dtype: object
This used to do the trick:
totalHist['Datez'] = pd.to_datetime(totalHist['Datez'], format='%d-%m-%Y')
totalHist.dtypes
which now is giving me:
KeyError: 'Datez'
During handling of the above exception, another exception occurred:
How can I fix this? I am doing this groupby before trying:
totalHist = df.groupby('Date', as_index = False).agg({"Trading_Value": "sum"})
totalHist.head()
totalHist.columns.values[[0, 1]] = ['Datez', 'Volumez']
totalHist.head()
You can just use .rename() to rename your columns
Generate some data (in same format as OP)
d = ['1/1/2018','1/2/2018','1/3/2018',
'1/3/2018','1/4/2018','1/2/2018','1/1/2018','1/5/2018']
df = pd.DataFrame(d, columns=['Date'])
df['Trading_Value'] = [1000,1005,1001,1001,1002,1009,1010,1002]
print(df)
Date Trading_Value
0 1/1/2018 1000
1 1/2/2018 1005
2 1/3/2018 1001
3 1/3/2018 1001
4 1/4/2018 1002
5 1/2/2018 1009
6 1/1/2018 1010
7 1/5/2018 1002
GROUP BY
totalHist = df.groupby('Date', as_index = False).agg({"Trading_Value": "sum"})
print(totalHist.head())
Date Trading_Value
0 1/1/2018 2010
1 1/2/2018 2014
2 1/3/2018 2002
3 1/4/2018 1002
4 1/5/2018 1002
Rename columns
totalHist.rename(columns={'Date':'Datez','totalHist':'Volumez'}, inplace=True)
print(totalHist)
Datez Trading_Value
0 1/1/2018 2010
1 1/2/2018 2014
2 1/3/2018 2002
3 1/4/2018 1002
4 1/5/2018 1002
Finally, convert to datetime
totalHist['Datez'] = pd.to_datetime(totalHist['Datez'])
print(totalHist.dtypes)
Datez datetime64[ns]
Trading_Value int64
dtype: object
This was done with python --version = 3.6.7 and pandas (0.23.4).
I have in SQL Server a historical return table by date and asset Id like this:
[Date] [Asset] [1DRet]
jan asset1 0.52
jan asset2 0.12
jan asset3 0.07
feb asset1 0.41
feb asset2 0.33
feb asset3 0.21
...
So I need to calculate the correlation matrix for a given date range for all assets combinations: A1,A2 ; A1,A3 ; A2,A3
Im using pandas and in my SQL Select Where I'm filtering tha date range and ordering it by date.
I'm trying to do it using pandas df.corr(), numpy.corrcoef and Scipy but not able to do it for my n-variable dataframe
I see some example but it's always for a dataframe where you have an asset per column and one row per day.
This my code block where I'm doing it:
qryRet = "Select * from IndexesValue where Date > '20100901' and Date < '20150901' order by Date"
result = conn.execute(qryRet)
df = pd.DataFrame(data=list(result),columns=result.keys())
df1d = df[['Date','Id_RiskFactor','1DReturn']]
corr = df1d.set_index(['Date','Id_RiskFactor']).unstack().corr()
corr.columns = corr.columns.droplevel()
corr.index = corr.columns.tolist()
corr.index.name = 'symbol_1'
corr.columns.name = 'symbol_2'
print(corr)
conn.close()
For it I'm reciving this msg:
corr.columns = corr.columns.droplevel()
AttributeError: 'Index' object has no attribute 'droplevel'
**Print(df1d.head())**
Date Id_RiskFactor 1DReturn
0 2010-09-02 149 0E-12
1 2010-09-02 150 -0.004242875148
2 2010-09-02 33 0.000590000011
3 2010-09-02 28 0.000099999997
4 2010-09-02 34 -0.000010000000
**print(df.head())**
Date Id_RiskFactor Value 1DReturn 5DReturn
0 2010-09-02 149 0.040096000000 0E-12 0E-12
1 2010-09-02 150 1.736700000000 -0.004242875148 -0.013014321215
2 2010-09-02 33 2.283000000000 0.000590000011 0.001260000048
3 2010-09-02 28 2.113000000000 0.000099999997 0.000469999999
4 2010-09-02 34 0.615000000000 -0.000010000000 0.000079999998
**print(corr.columns)**
Index([], dtype='object')
Create a sample DataFrame:
import pandas as pd
import numpy as np
df = pd.DataFrame({'daily_return': np.random.random(15),
'symbol': ['A'] * 5 + ['B'] * 5 + ['C'] * 5,
'date': np.tile(pd.date_range('1-1-2015', periods=5), 3)})
>>> df
daily_return date symbol
0 0.011467 2015-01-01 A
1 0.613518 2015-01-02 A
2 0.334343 2015-01-03 A
3 0.371809 2015-01-04 A
4 0.169016 2015-01-05 A
5 0.431729 2015-01-01 B
6 0.474905 2015-01-02 B
7 0.372366 2015-01-03 B
8 0.801619 2015-01-04 B
9 0.505487 2015-01-05 B
10 0.946504 2015-01-01 C
11 0.337204 2015-01-02 C
12 0.798704 2015-01-03 C
13 0.311597 2015-01-04 C
14 0.545215 2015-01-05 C
I'll assume you've already filtered your DataFrame for the relevant dates. You then want a pivot table where you have unique dates as your index and your symbols as separate columns, with daily returns as the values. Finally, you call corr() on the result.
corr = df.set_index(['date','symbol']).unstack().corr()
corr.columns = corr.columns.droplevel()
corr.index = corr.columns.tolist()
corr.index.name = 'symbol_1'
corr.columns.name = 'symbol_2'
>>> corr
symbol_2 A B C
symbol_1
A 1.000000 0.188065 -0.745115
B 0.188065 1.000000 -0.688808
C -0.745115 -0.688808 1.000000
You can select the subset of your DataFrame based on dates as follows:
start_date = pd.Timestamp('2015-1-4')
end_date = pd.Timestamp('2015-1-5')
>>> df.loc[df.date.between(start_date, end_date), :]
daily_return date symbol
3 0.371809 2015-01-04 A
4 0.169016 2015-01-05 A
8 0.801619 2015-01-04 B
9 0.505487 2015-01-05 B
13 0.311597 2015-01-04 C
14 0.545215 2015-01-05 C
If you want to flatten your correlation matrix:
corr.stack().reset_index()
symbol_1 symbol_2 0
0 A A 1.000000
1 A B 0.188065
2 A C -0.745115
3 B A 0.188065
4 B B 1.000000
5 B C -0.688808
6 C A -0.745115
7 C B -0.688808
8 C C 1.000000