I am making a table, where i want to show that if there's no income, no expense can happen
it's a cumulative sum table
This is what I've
Incoming
Outgoing
Total
0
150
-150
10
20
-160
100
30
-90
50
70
-110
Required output
Incoming
Outgoing
Total
0
150
0
10
20
0
100
30
70
50
70
50
I've tried
df.clip(lower=0)
and
df['new_column'].apply(lambda x : df['outgoing']-df['incoming'] if df['incoming']>df['outgoing'])
That doesn't work as well
is there any other way?
Update:
A more straightforward approach inspired by your code using clip and without numpy:
diff = df['Incoming'].sub(df['Outgoing'])
df['Total'] = diff.mul(diff.ge(0).cumsum().clip(0, 1)).cumsum()
print(df)
# Output:
Incoming Outgoing Total
0 0 150 0
1 10 20 0
2 100 30 70
3 50 70 50
Old answer:
Find the row where the balance is positive for the first time then compute the cumulative sum from this point:
start = np.where(df['Incoming'] - df['Outgoing'] >= 0)[0][0]
df['Total'] = df.iloc[start:]['Incoming'].sub(df.iloc[start:]['Outgoing']) \
.cumsum().reindex(df.index, fill_value=0)
Output:
>>> df
Incoming Outgoing Total
0 0 150 0
1 10 20 0
2 100 30 70
3 50 70 50
IIUC, you can check when Incoming is greater than Outgoing using np.where and assign a helper column. Then you can check when this new column is not null, using notnull(), calculate the difference, and use cumsum() on the result:
df['t'] = np.where(df['Incoming'].ge(df['Outgoing']),0,np.nan)
df['t'].ffill(axis=0,inplace=True)
df['Total'] = np.where(df['t'].notnull(),(df['Incoming'].sub(df['Outgoing'])),df['t'])
df['Total'] = df['Total'].cumsum()
df.drop('t',axis=1,inplace=True)
This will give back:
Incoming Outgoing Total
0 0 150 NaN
1 10 20 NaN
2 100 30 70.0
3 50 70 50.0
I have following dataframe. I want to iterate over every row and compare the score column, if the value is >= value present in cut_off list.
seq score status
7 TTGTTCTCTGTGTATTTCAGGCT 10.42 positive
56 CAGGTGAGA 9.22 positive
64 AATTCCTGTGGACTTTCAAGTAT 1.23 positive
116 AAGGTATAT 7.84 positive
145 AAGGTAATA 8.49 positive
172 TGGGTAGGT 6.86 positive
204 CAGGTAGAG 7.10 positive
214 GCGTTTCTTGAATCCAGCAGGGA 3.58 positive
269 GAGGTAATG 8.73 positive
274 CACCCATTCCTGTACCTTAGGTA 8.96 positive
325 GCCGTAAGG 5.46 positive
356 GAGGTGAGG 8.41 positive
cut_off = range(0, 11)
The code I tried so far is:
cutoff_list_pos = []
number_list_pos = []
cut_off = range(0, int(new_df['score'].max())+1)
for co in cut_off:
for df in df_elements:
val = (df['score']>=co).value_counts()
cutoff_list_pos.append(co)
number_list_pos.append(val)
The desired output is:
cutoff true false
0 0 12.0 0
1 1 12.0 0
and so on..
If the score is >= to the value in cut_off, it should assign the row as true else false.
You can use parameter keys in concat by values of cutoff_list_pos, then transpose and convert index to column by DataFrame.reset_index:
df = (pd.concat(number_list_pos, axis=1, keys=cutoff_list_pos, sort=False)
.T
.rename_axis('cutoff')
.reset_index())
Another pandas implementation:
res_df = pd.DataFrame(columns=['cutoff', 'true'])
for i in range(1,int(df['score'].max()+1)):
temp_df = pd.DataFrame(data={'cutoff': i, 'true': (df['score']>=i).sum()}, index=[i])
res_df = pd.concat([res_df, temp_df])
res_df
cutoff true
1 1 12
2 2 11
3 3 11
4 4 10
5 5 10
6 6 9
7 7 8
8 8 6
9 9 2
10 10 1
I'm trying to sort a data frame based on groups meeting conditions.
The I'm getting a syntax error for the way I'm sorting the groups.
And I'm losing the initial order of the data frame before attempting the above.
This is the order of sorting that I'm trying to achieve:
1) Sort on First and Test columns.
2) Test==1 groups, sort on Secondary then by Final column.
---Test==0 groups, sort on Final column only.
import pandas as pd
df=pd.DataFrame({"First":[100,100,100,100,100,100,200,200,200,200,200],"Test":[1,1,1,0,0,0,0,1,1,1,0],"Secondary":[.1,.1,.1,.2,.2,.3,.3,.3,.3,.4,.4],"Final":[1.1,2.2,3.3,4.4,5.5,6.6,7.7,8.8,9.9,10.10,11.11]})
def sorter(x):
if x["Test"]==1:
x.sort_values(['Secondary','Final'], inplace=True)
else:
x=x.sort_values('Final', inplace=True)
df=df.sort_values(["First","Test"],ascending=[False, False]).reset_index(drop=True)
df.groupby(['First','Test']).apply(lambda x: sorter(x))
df
Expected result:
First Test Secondary Final
200 1 0.4 10.1
200 1 0.3* 9.9*
200 1 0.3* 8.8*
200 0 0.4 11.11*
200 0 0.3 7.7*
100 1 0.5 2.2
100 1 0.1* 3.3*
100 1 0.1* 1.1*
100 0 0.3 6.6*
100 0 0.2 5.5*
100 0 0.2 4.4*
You can try of sorting in descending order without groupby,
w.r.t sequence you gave, the order of sorting will change.will it work for you
df=pd.DataFrame({"First":[100,100,100,100,100,100,200,200,200,200,200],"Test":[1,1,1,0,0,0,0,1,1,1,0],"Secondary":[.1,.5,.1,.9,.4,.1,.3,.3,.3,.4,.4],"Final":[1.1,2.2,3.3,4.4,5.5,6.6,7.7,8.8,9.9,10.10,11.11]})
df = df.groupby(['First','Test']).apply(lambda x: x.sort_values(['First','Test','Secondary','Final'],ascending=False) if x.iloc[0]['Test']==1 else x.sort_values(['First','Test','Final'],ascending=False)).reset_index(drop=True)
df.sort_values(['First','Test'],ascending=[True,False])
Out:
Final First Secondary Test
3 2.20 100 0.5 1
4 3.30 100 0.1 1
5 1.10 100 0.1 1
0 6.60 100 0.1 0
1 5.50 100 0.4 0
2 4.40 100 0.9 0
8 10.10 200 0.4 1
9 9.90 200 0.3 1
10 8.80 200 0.3 1
6 11.11 200 0.4 0
7 7.70 200 0.3 0
The trick was to sort subsets separately and replace the values in the original df.
This came up in other solutions to pandas sorting problems.
import pandas as pd
df=pd.DataFrame({"First":[100,100,100,100,100,100,200,200,200,200,200],"Test":[1,1,1,0,0,0,0,1,1,1,0],"Secondary":[.1,.5,.1,.9,.4,.1,.3,.3,.3,.4,.4],"Final":[1.1,2.2,3.3,4.4,5.5,6.6,7.7,8.8,9.9,10.10,11.11]})
df.sort_values(['First','Test','Secondary','Final'],ascending=False, inplace=True)
index_subset=df[df["Test"]==0].index
sorted_subset=df[df["Test"]==0].sort_values(['First','Final'],ascending=False)
df.loc[index_subset,:]=sorted_subset.values
print(df)
I am reading a large file that contains ~9.5 million rows x 16 cols.
I am interested in retrieving a representative sample, and since the data is organized by time, I want to do this by selecting every 500th element.
I am able to load the data, and then select every 500th row.
My question: Can I immediately read every 500th element (using.pd.read_csv() or some other method), without having to read first and then filter my data?
Question 2: How would you approach this problem if the date column was not ordered? At the moment, I am assuming it's ordered by date, but all data is prone to errors.
Here is a snippet of what the data looks like (first five rows) The first 4 rows are out of order, bu the remaining dataset looks ordered (by time):
VendorID tpep_pickup_datetime tpep_dropoff_datetime passenger_count trip_distance RatecodeID store_and_fwd_flag PULocationID DOLocationID payment_type fare_amount extra mta_tax tip_amount tolls_amount improvement_surcharge total_amount
0 1 2017-01-09 11:13:28 2017-01-09 11:25:45 1 3.30 1 N 263 161 1 12.5 0.0 0.5 2.00 0.00 0.3 15.30
1 1 2017-01-09 11:32:27 2017-01-09 11:36:01 1 0.90 1 N 186 234 1 5.0 0.0 0.5 1.45 0.00 0.3 7.25
2 1 2017-01-09 11:38:20 2017-01-09 11:42:05 1 1.10 1 N 164 161 1 5.5 0.0 0.5 1.00 0.00 0.3 7.30
3 1 2017-01-09 11:52:13 2017-01-09 11:57:36 1 1.10 1 N 236 75 1 6.0 0.0 0.5 1.70 0.00 0.3 8.50
4 2 2017-01-01 00:00:00 2017-01-01 00:00:00 1 0.02 2 N 249 234 2 52.0 0.0 0.5 0.00 0.00 0.3 52.80
Can I immediately read every 500th element (using.pd.read_csv() or some other method), without having to read first and then filter my data?
Something you could do is to use the skiprows parameter in read_csv, which accepts a list-like argument to discard the rows of interest (and thus, also select). So you could create a np.arange with a length equal to the amount of rows to read, and remove every 500th element from it using np.delete, so this way we'll only be reading every 500th row:
n_rows = 9.5e6
skip = np.arange(n_rows)
skip = np.delete(skip, np.arange(0, n_rows, 500))
df = pd.read_csv('my_file.csv', skiprows = skip)
Can I immediately read every 500th element (using.pd.read_csv() or some other method), without having to read first and then filter my data?
First get the length of the file by a custom function, remove each 500 row by numpy.setdiff1d and pass to the skiprows parameter in read_csv:
#https://stackoverflow.com/q/845058
def file_len(fname):
with open(fname) as f:
for i, l in enumerate(f):
pass
return i + 1
len_of_file = file_len('test.csv')
print (len_of_file)
skipped = np.setdiff1d(np.arange(len_of_file), np.arange(0,len_of_file,500))
print (skipped)
df = pd.read_csv('test.csv', skiprows=skipped)
How would you approach this problem if the date column was not ordered? At the moment, I am assuming it's ordered by date, but all data is prone to errors.
The idea is read only the datetime column by parameter usecols, and then sort and select each 500 index value, get the difference and pass again to parameter skiprows:
def file_len(fname):
with open(fname) as f:
for i, l in enumerate(f):
pass
return i + 1
len_of_file = file_len('test.csv')
df1 = pd.read_csv('test.csv',
usecols=['tpep_pickup_datetime'],
parse_dates=['tpep_pickup_datetime'])
sorted_idx = (df1['tpep_pickup_datetime'].sort_values()
.iloc[np.arange(0,len_of_file,500)].index)
skipped = np.setdiff1d(np.arange(len_of_file), sorted_idx)
print (skipped)
df = pd.read_csv('test.csv', skiprows=skipped).sort_values(by=['tpep_pickup_datetime'])
use a lambda with skiprows:
pd.read_csv(path, skiprows=lambda i: i % N)
to skip every N rows.
source: https://pandas.pydata.org/docs/reference/api/pandas.read_csv.html
You can use csv module return a iterator and use itertools.cycle to select every nth row.
import csv
from itertools import cycle
source_file='D:/a.txt'
cycle_size=500
chooser = (x == 0 for x in cycle(range(cycle_size)))
with open(source_file) as f1:
rdr = csv.reader(f1)
data = [row for pick, row in zip(chooser, rdr) if pick]
I start with a simple
df = pd.DataFrame({'units':[30,20]})
And I get
units
0 30
1 20
I then add a row to total the column:
my_sum = df.sum()
df = df.append(my_sum, ignore_index=True)
Finally, I add a column to calculate percentages off of the 'units' column:
df['pct'] = df.units / df.units[:-1].sum()
ending with this:
units pct
0 30 0.6
1 20 0.4
2 50 1.0
So far so good - but now the question: I want to change the middle number of units from 20 to, for example, 30. I can use this:
df3.iloc[1, 0] = 40
or
df3.iat[1, 0] = 40
which change the cell, but the calculated values at both the last row and second column don't change to reflect it:
units pct
0 30 0.6
1 40 0.4
2 50 1.0
How do I force these calculated values to adjust following the change in that particular cell?
Make a function that calculates it
def f(df):
return df.append(df.sum(), ignore_index=True).assign(
pct=lambda d: d.units / d.units.iat[-1])
df.iat[1, 0] = 40
f(df)
units pct
0 30 0.428571
1 40 0.571429
2 70 1.000000