I think I have a problem with time calculation.
I want to run this code on a DataFrame of 320 000 lines, 6 columns:
index_data = data["clubid"].index.tolist()
for i in index_data:
for j in index_data:
if data["clubid"][i] == data["clubid"][j]:
if data["win_bool"][i] == 1:
if (data["startdate"][i] >= data["startdate"][j]) & (
data["win_bool"][j] == 1
):
NW_tot[i] += 1
else:
if (data["startdate"][i] >= data["startdate"][j]) & (
data["win_bool"][j] == 0
):
NL_tot[i] += 1
The objective is to determine the number of wins and the number of losses from a given match taking into account the previous match, this for every clubid.
The problem is, I don't get an error, but I never obtain any results either.
When I tried with a smaller DataFrame ( data[0:1000] ) I got a result in 13 seconds. This is why I think it's a time calculation problem.
I also tried to first use a groupby("clubid"), then do my for loop into every group but I drowned myself.
Something else that bothers me, I have at least 2 lines with the exact same date/hour, because I have at least two identical dates for 1 match. Because of this I can't put the date in index.
Could you help me with these issues, please?
As I pointed out in the comment above, I think you can simply sum the vector of win_bool by group. If the dates are sorted this should be equivalent to your loop, correct?
import pandas as pd
dat = pd.DataFrame({
"win_bool":[0,0,1,0,1,1,1,0,1,1,1,1,1,1,0],
"clubid": [1,1,1,1,1,1,1,2,2,2,2,2,2,2,2],
"date" : [1,2,1,2,3,4,5,1,2,1,2,3,4,5,6],
"othercol":["a","b","b","b","b","b","b","b","b","b","b","b","b","b","b"]
})
temp = dat[["clubid", "win_bool"]].groupby("clubid")
NW_tot = temp.sum()
NL_tot = temp.count()
NL_tot = NL_tot["win_bool"] - NW_tot["win_bool"]
If you have duplicate dates that inflate the counts, you could first drop duplicates by dates (within groups):
# drop duplicate dates
temp = dat.drop_duplicates(["clubid", "date"])[["clubid", "win_bool"]].groupby("clubid")
Could anyone help me ?
I want to sum the values with the format:
print (...+....+)
for example:
a b
France 2
Italie 15
Croatie 7
I want to make the sum of France and Croatie.
Thank you for your help !
One of possible solutions:
set column a as the index,
using loc select rows for the "wanted" values,
take column b,
sum the values found.
So the code can be:
result = df.set_index('a').loc[['France', 'Croatie']].b.sum()
Note double square brackets. The outer pair is the "container" of index values
passed to loc.
The inner part, and what is inside, is a list of values.
To subtract two sums (one for some set of countries and the second for another set),
you can run e.g.:
wrk = df.set_index('a').b
result = wrk.loc[['Italie', 'USA']].sum() - wrk.loc[['France', 'Croatie']].sum()
I can create an index vs the previous year when I have just one item, but I'm trying to figure out how to do this when I have multiple items. Here is my data set:
rng = pd.date_range('1/1/2011', periods=3, freq='Y')
rng = np.repeat(rng,3)
country = ["USA","Brazil","Japan"]*3
df = pd.DataFrame({'Country':country,'date':rng,'value':range(20,29)})
If I only had one item/country I can do something like this:
df['pct_iya'] = 100*(df['value'].pct_change()+1)
I'm trying to get this to work with multiple items. Here is the expected result:
Maybe this could work with a groupby, but my attempt did not work...
df['pct_iya2'] = df.groupby(['Country','date'])['value'].pct_change()
Answer: Use a group by (excluding date) and than add one to the percent change (ex +15percent goes from .15 to 1.15), then multiple you 100.
df['pct_iya'] = 100*(df.groupby(['Country'])['value'].pct_change()+1)
I am working with several CSV's that first N columns are information and then the next Ms (M is big) columns are information regarding a date.
This is the dataframe picture
I need to set just the columns between N+1 to N+M - 1 columns name to date format.
I tried this, in this case N+1 = 5, no matter M, I suppose that I can use -1 to not affect the last column name.
ContDiarios.columns[5:-1] = pd.to_datetime(ContDiarios.columns[5:-1])
but I get the following error:
TypeError: Index does not support mutable operations
The way you are doing is not feasable. Please try this way
def convert(x):
try:
return pd.to_datetime(x)
except:
return x
x.columns = map(convert,x.columns)
Or you can also use df.rename property to convert it.
I have a data frame that is a single row of numerical values and I want to know if any of those values is greater than 2 and if so create a new column with the word 'Diff'
Col_,F_1,F_2
1,5,0
My dataframe is diff_df. Here is one thing I tried
c = diff_df >2
if c.any():
diff_df['difference']='Difference'
If I were to print c. it would be
Col_,F_1,F_2
False,True,False
I have tried c.all() and many iterations of other things. Clearly my inexperience is holding me back and google is not helping in this regards. Everything I try is either "The truth value of a Series (or Data Frame) is ambiguous use a.any(), a.all()...." Any help would be appreciated.
Since it is only one row, take the .max().max() of the dataframe. With one .max() you are going to get the .max() of each column. The second .max() takes the max of all the columns.
if diff_df.max().max() > 2: diff_df['difference']='Difference'
output:
Col_ F_1 F_2 difference
0 1 5 0 Difference
Use .loc accessor and .gt() to query and at the same time create new column and populate it
df.loc[df.gt(2).any(1), "difference"] = 'Difference'
Col_ F_1 F_2 difference
0 1 5 0 Difference
In addition to David's reponse you may also try this:
if ((df > 2).astype(int)).sum(axis=1).values[0] == 1:
df['difference']='Difference'