I have a input file like this:
j,z,b,bsy,afj,upz,343,13,ruhwd
u,i,a,dvp,ibt,dxv,154,00,adsif
t,a,a,jqj,dtd,yxq,540,49,kxthz
j,z,b,bsy,afj,upz,343,13,ruhwd
u,i,a,dvp,ibt,dxv,154,00,adsif
t,a,a,jqj,dtd,yxq,540,49,kxthz
c,u,g,nfk,ekh,trc,085,83,xppnl
For every unique value of Column1, I need to find out the sum of column7
Similarly, for every unique value of Column2, I need to find out the sum of column7
Output for 1 should be like:
j,686
u,308
t,98
c,83
Output for 2 should be like:
z,686
i,308
a,98
u,83
I am fairly new in Python. How can I achieve the above?
This could be done using Python's Counter and csv library as follows:
from collections import Counter
import csv
c1 = Counter()
c2 = Counter()
with open('input.csv') as f_input:
for cols in csv.reader(f_input):
col7 = int(cols[6])
c1[cols[0]] += col7
c2[cols[1]] += col7
print "Column 1"
for value, count in c1.iteritems():
print '{},{}'.format(value, count)
print "\nColumn 2"
for value, count in c2.iteritems():
print '{},{}'.format(value, count)
Giving you the following output:
Column 1
c,85
j,686
u,308
t,1080
Column 2
i,308
a,1080
z,686
u,85
A Counter is a type of Python dictionary that is useful for counting items automatically. c1 holds all of the column 1 entries and c2 holds all of the column 2 entries. Note, Python numbers lists starting from 0, so the first entry in a list is [0].
The csv library loads each line of the file into a list, with each entry in the list representing a different column. The code takes column 7 (i.e. cols[6]) and converts it into an integer, as all columns are held as strings. It is then added to the counter using either the column 1 or 2 value as the key. The result is two dictionaries holding the totaled counts for each key.
You can use pandas:
df = pd.read_csv('my_file.csv', header=None)
print(df.groupby(0)[6].sum())
print(df.groupby(1)[6].sum())
Output:
0
c 85
j 686
t 1080
u 308
Name: 6, dtype: int64
1
a 1080
i 308
u 85
z 686
Name: 6, dtype: int64
The data frame should look like this:
print(df.head())
Output:
0 1 2 3 4 5 6 7 8
0 j z b bsy afj upz 343 13 ruhwd
1 u i a dvp ibt dxv 154 0 adsif
2 t a a jqj dtd yxq 540 49 kxthz
3 j z b bsy afj upz 343 13 ruhwd
4 u i a dvp ibt dxv 154 0 adsif
You can also use your own names for the columns. Like c1, c2, ... c9:
df = pd.read_csv('my_file.csv', index_col=False, names=['c' + str(x) for x in range(1, 10)])
print(df)
Output:
c1 c2 c3 c4 c5 c6 c7 c8 c9
0 j z b bsy afj upz 343 13 ruhwd
1 u i a dvp ibt dxv 154 0 adsif
2 t a a jqj dtd yxq 540 49 kxthz
3 j z b bsy afj upz 343 13 ruhwd
4 u i a dvp ibt dxv 154 0 adsif
5 t a a jqj dtd yxq 540 49 kxthz
6 c u g nfk ekh trc 85 83 xppnl
Now, group by column 1 c1 or column c2 and sum up column 7 c7:
print(df.groupby(['c1'])['c7'].sum())
print(df.groupby(['c2'])['c7'].sum())
Output:
c1
c 85
j 686
t 1080
u 308
Name: c7, dtype: int64
c2
a 1080
i 308
u 85
z 686
Name: c7, dtype: int64
SO isn't supposed to be a code writing service, but I had a few minutes. :) Without Pandas you can do it with the CSV module;
import csv
def sum_to(results, key, add_value):
if key not in results:
results[key] = 0
results[key] += int(add_value)
column1_results = {}
column2_results = {}
with open("input.csv", 'rt') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
sum_to(column1_results, row[0], row[6])
sum_to(column2_results, row[1], row[6])
print column1_results
print column2_results
Results:
{'c': 85, 'j': 686, 'u': 308, 't': 1080}
{'i': 308, 'a': 1080, 'z': 686, 'u': 85}
Your expected results don't seem to match the math that Mike's answer and mine got using your spec. I'd double check that.
Related
I am trying to understand the results I got for a fake dataset. I have two independent variables, hours, type and response pain.
First question: How was 82.46721 calculated as the lsmeans for the first type?
Second question: Why is the standard error exactly the same (8.24003) for both types?
Third question: Why is the degrees of freedom 3 for both types?
data = data.frame(
type = c("A", "A", "A", "B", "B", "B"),
hours = c(60,72,61, 54,68,66),
# pain = c(85,95,69, 73, 29, 30)
pain = c(85,95,69, 85,95,69)
)
model = lm(pain ~ hours + type, data = data)
lsmeans(model, c("type", "hours"))
> data
type hours pain
1 A 60 85
2 A 72 95
3 A 61 69
4 B 54 85
5 B 68 95
6 B 66 69
> lsmeans(model, c("type", "hours"))
type hours lsmean SE df lower.CL upper.CL
A 63.5 82.46721 8.24003 3 56.24376 108.6907
B 63.5 83.53279 8.24003 3 57.30933 109.7562
Try this:
newdat <- data.frame(type = c("A", "B"), hours = c(63.5, 63.5))
predict(model, newdata = newdat)
An important thing to note here is that your model has hours as a continuous predictor, not a factor.
I have a function that takes all, non-distinct, MatchId and (xG_Team1 vs xG_Team2, paired) and gives an output of as an array. which then summed up to be sse constant.
The problem with the function is it iterates through each row, duplicating MatchId. I want to stop this.
For each distinct MatchId I need the corresponding home and away goals as a list. I.e. Home_Goal and Away_Goal to be used in each iteration. from Home_Goal_time and Away_Goal_time columns of the dataframe. The list below doesn't seem to work.
MatchId Event_Id EventCode Team1 Team2 Team1_Goals
0 842079 2053 Goal Away Huachipato Cobresal 0
1 842079 2053 Goal Away Huachipato Cobresal 0
2 842080 1029 Goal Home Slovan lava 3
3 842080 1029 Goal Home Slovan lava 3
4 842080 2053 Goal Away Slovan lava 3
5 842080 1029 Goal Home Slovan lava 3
6 842634 2053 Goal Away Rosario Boca Juniors 0
7 842634 2053 Goal Away Rosario Boca Juniors 0
8 842634 2053 Goal Away Rosario Boca Juniors 0
9 842634 2054 Cancel Goal Away Rosario Boca Juniors 0
Team2_Goals xG_Team1 xG_Team2 CurrentPlaytime Home_Goal_Time Away_Goal_Time
0 2 1.79907 1.19893 2616183 0 87
1 2 1.79907 1.19893 3436780 0 115
2 1 1.70662 1.1995 3630545 121 0
3 1 1.70662 1.1995 4769519 159 0
4 1 1.70662 1.1995 5057143 0 169
5 1 1.70662 1.1995 5236213 175 0
6 2 0.82058 1.3465 2102264 0 70
7 2 0.82058 1.3465 4255871 0 142
8 2 0.82058 1.3465 5266652 0 176
9 2 0.82058 1.3465 5273611 0 0
For example MatchId = 842079, Home_goal =[], Away_Goal = [87, 115]
x1 = [1,0,0]
x2 = [0,1,0]
x3 = [0,0,1]
m = 1 ,arbitrary constant used to optimise sse.
k = 196
total_timeslot = 196
Home_Goal = [] # No Goal
Away_Goal = [] # No Goal
def sum_squared_diff(x1, x2, x3, y):
ssd = []
for k in range(total_timeslot): # k will take multiple values
if k in Home_Goal:
ssd.append(sum((x2 - y) ** 2))
elif k in Away_Goal:
ssd.append(sum((x3 - y) ** 2))
else:
ssd.append(sum((x1 - y) ** 2))
return ssd
def my_function(row):
xG_Team1 = row.xG_Team1
xG_Team2 = row.xG_Team2
return np.array([1-(xG_Team1*m + xG_Team2*m)/k, xG_Team1*m/k, xG_Team2*m/k])
results = df.apply(lambda row: sum_squared_diff(x1, x2, x3, my_function(row)), axis=1)
results
sum(results.sum())
For the three matches above the desire outcome should look like the following.
If I need an individual sse, sum(sum_squared_diff(x1, x2, x3, y)) gives me the following.
MatchId = 842079 = 3.984053038520635
MatchId = 842080 = 7.882189570700502
MatchId = 842080 = 5.929085973050213
Given the size of the original data, realistically I am after the total sum of the sse. For the above sample data, simply adding up the values give total sse=17.79532858227135.` Once I achieve this, then I will try to optimise the sse based on this figure by updating the arbitrary value m.
Here are the lists i hoped the function will iterate over.
Home_scored = s.groupby('MatchId')['Home_Goal_time'].apply(list)
Away_scored = s.groupby('MatchId')['Away_Goal_Time'].apply(list)
type(HomeGoal)
pandas.core.series.Series
Then convert it to lists.
Home_Goal = Home_scored.tolist()
Away_Goal = Away_scored.tolist()
type(Home_Goal)
list
Home_Goal
Out[303]: [[0, 0], [121, 159, 0, 175], [0, 0, 0, 0]]
Away_Goal
Out[304]: [[87, 115], [0, 0, 169, 0], [70, 142, 176, 0]]
But the function still takes Home_Goal and Away_Goal as empty list.
If you only want to consider one MatchId at a time you should .groupby('MatchID') first
df.groupby('MatchID').apply(...)
I have the following data frame:
id my_year my_month waiting_time target
001 2018 1 95 1
002 2018 1 3 3
003 2018 1 4 0
004 2018 1 40 1
005 2018 2 97 1
006 2018 2 3 3
007 2018 3 4 0
008 2018 3 40 1
I want to groupby my_year and my_month, then in each group I want to compute the my_rate based on
(# of records with waiting_time <= 90 and target = 1)/ total_records in the group
i.e. I am expecting output like:
my_year my_month my_rate
2018 1 0.25
2018 2 0.0
2018 3 0.5
I wrote the following code to compute the desired value my_rate:
def my_rate(data):
waiting_time_list = data['waiting_time']
target_list = data['target']
total = len(data)
my_count = 0
for i in range(len(data)):
if total_waiting_time_list[i] <= 90 and target_list[i] == 1:
my_count += 1
rate = float(my_count)/float(total)
return rate
df.groupby(['my_year','my_month']).apply(my_rate)
However, I got the following error:
KeyError 0
KeyErrorTraceback (most recent call last)
<ipython-input-29-5c4399cefd05> in <module>()
17
---> 18 df.groupby(['my_year','my_month']).apply(my_rate)
/opt/conda/envs/python2/lib/python2.7/site-packages/pandas/core/groupby.pyc in apply(self, func, *args, **kwargs)
714 # ignore SettingWithCopy here in case the user mutates
715 with option_context('mode.chained_assignment', None):
--> 716 return self._python_apply_general(f)
717
718 def _python_apply_general(self, f):
/opt/conda/envs/python2/lib/python2.7/site-packages/pandas/core/groupby.pyc in _python_apply_general(self, f)
718 def _python_apply_general(self, f):
719 keys, values, mutated = self.grouper.apply(f, self._selected_obj,
--> 720 self.axis)
721
722 return self._wrap_applied_output(
/opt/conda/envs/python2/lib/python2.7/site-packages/pandas/core/groupby.pyc in apply(self, f, data, axis)
1727 # group might be modified
1728 group_axes = _get_axes(group)
-> 1729 res = f(group)
1730 if not _is_indexed_like(res, group_axes):
1731 mutated = True
<ipython-input-29-5c4399cefd05> in conversion_rate(data)
8 #print total_waiting_time_list[i], target_list[i]
9 #print i, total_waiting_time_list[i], target_list[i]
---> 10 if total_waiting_time_list[i] <= 90:# and target_list[i] == 1:
11 convert_90_count += 1
12 #print 'convert ', convert_90_count
/opt/conda/envs/python2/lib/python2.7/site-packages/pandas/core/series.pyc in __getitem__(self, key)
599 key = com._apply_if_callable(key, self)
600 try:
--> 601 result = self.index.get_value(self, key)
602
603 if not is_scalar(result):
/opt/conda/envs/python2/lib/python2.7/site-packages/pandas/core/indexes/base.pyc in get_value(self, series, key)
2426 try:
2427 return self._engine.get_value(s, k,
-> 2428 tz=getattr(series.dtype, 'tz', None))
2429 except KeyError as e1:
2430 if len(self) > 0 and self.inferred_type in ['integer', 'boolean']:
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_value (pandas/_libs/index.c:4363)()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_value (pandas/_libs/index.c:4046)()
pandas/_libs/index.pyx in pandas._libs.index.IndexEngine.get_loc (pandas/_libs/index.c:5085)()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item (pandas/_libs/hashtable.c:13913)()
pandas/_libs/hashtable_class_helper.pxi in pandas._libs.hashtable.Int64HashTable.get_item (pandas/_libs/hashtable.c:13857)()
KeyError: 0
Any idea what I did wrong here? And how do I fix it? Thanks!
I believe better is use mean of boolean mask per groups:
def my_rate(x):
return ((x['waiting_time'] <= 90) & (x['target'] == 1)).mean()
df = df.groupby(['my_year','my_month']).apply(my_rate).reset_index(name='my_rate')
print (df)
my_year my_month my_rate
0 2018 1 0.25
1 2018 2 0.00
2 2018 3 0.50
Any idea what I did wrong here?
Problem is waiting_time_list and target_list are not lists, but Series:
waiting_time_list = data['waiting_time']
target_list = data['target']
print (type(waiting_time_list))
<class 'pandas.core.series.Series'>
print (type(target_list))
<class 'pandas.core.series.Series'>
So if want indexing it failed, because in second group are indices 4,5, not 0,1.
if waiting_time_list[i] <= 90 and target_list[i] == 1:
For avoid it is possible convert Series to list:
waiting_time_list = data['waiting_time'].tolist()
target_list = data['target'].tolist()
I have a dataframe(df1) as following:
datetime m d 1d 2d 3d
2014-01-01 1 1 2 2 3
2014-01-02 1 2 3 4 3
2014-01-03 1 3 1 2 3
...........
2014-12-01 12 1 2 2 3
2014-12-31 12 31 2 2 3
Also I have another dataframe(df2) as following:
datetime m d
2015-01-02 1 2
2015-01-03 1 3
...........
2015-12-01 12 1
2015-12-31 12 31
I want to merge the 1d 2d 3d columns value of df1 to df2.
There are two conditions:
(1) only m and d are the same in both df1 and df2 can merge.
(2) if the index of df2 index % 30 ==0 don't merge, the value of 1d 2d 3d of these index can be Nan.
I mean I want the new dataframe of df2 like as following:
datetime m d 1d 2d 3d
2015-01-02 1 2 Nan Nan Nan
2015-01-03 1 3 1 2 3
...........
2015-12-01 12 1 2 2 3
2015-12-31 12 31 2 2 3
Thanks in advance!
I think you need add NaNs by loc and then merge with left join:
np.random.seed(10)
N = 365
rng = pd.date_range('2015-01-01', periods=N)
df_tr_2014 = pd.DataFrame(np.random.randint(10, size=(N, 3)), index=rng).reset_index()
df_tr_2014.columns = ['datetime','7d','15d','20d']
df_tr_2014.insert(1,'month', df_tr_2014['datetime'].dt.month)
df_tr_2014.insert(2,'day_m', df_tr_2014['datetime'].dt.day)
#print (df_tr_2014.head())
N = 366
rng = pd.date_range('2016-01-01', periods=N)
df_te = pd.DataFrame(index=rng)
df_te['month'] = df_te.index.month
df_te['day_m'] = df_te.index.day
df_te = df_te.reset_index()
#print (df_te.tail())
df2 = df_te.copy()
df1 = df_tr_2014.copy()
df1 = df1.set_index('datetime')
df1.index += pd.offsets.DateOffset(years=1)
#correct 29 February
y = df1.index[0].year
df1 = df1.reindex(pd.date_range(pd.datetime(y,1,1), pd.datetime(y,12,31)))
idx = df1.index[(df1.index.month == 2) & (df1.index.day == 29)]
df1.loc[idx, :] = df1.loc[idx - pd.Timedelta(1, unit='d'), :].values
df1.loc[idx, 'day_m'] = idx.day
df1[['month','day_m']] = df1[['month','day_m']].astype(int)
df1[['7d','15d', '20d']] = df1[['7d','15d', '20d']].astype(float)
df1.loc[np.arange(len(df1.index)) % 30 == 0, ['7d','15d','20d']] = 0
df1 = df1.reset_index()
print (df1.iloc[57:62])
index month day_m 7d 15d 20d
57 2016-02-27 2 27 2.0 0.0 1.0
58 2016-02-28 2 28 2.0 3.0 5.0
59 2016-02-29 2 29 2.0 3.0 5.0
60 2016-03-01 3 1 0.0 0.0 0.0
61 2016-03-02 3 2 7.0 6.0 9.0
Why don't you just remove the rows in df1 that don't match (m, d) pairs in df2?
df_new = df2.drop(df2[(not ((df2.m == df1.m) & (df2.n == df1.n)).any()) or (df2.index % 30 == 0)].index)
Or something along those lines.
Link to a related answer.
I'm not enormously familiar with Pandas and have not tested the above example.
df_te is df2
df_tr_2014 is df1
7d 15d 20 is 1d 2d 3d respectively in question. size_df_te is the length of df_te, month and day_m are m, d in df2
df_te['7d'] = 0
df_te['15d'] = 0
df_te['20d'] = 0
mj = 0
dj = 0
for i in range(size_df_te):
if i%30 != 0:
m = df_te.loc[i,'month']
d = df_te.loc[i,'day_m']
if (m== 2) & (d == 29):
m = 2
d = 28
dk_7 = df_tr_2014.loc[(df_tr_2014['month']==m) & (df_tr_2014['day_m']==d)]['7d']
dk_15 = df_tr_2014.loc[(df_tr_2014['month']==m) & (df_tr_2014['day_m']==d)]['15d']
dk_20 = df_tr_2014.loc[(df_tr_2014['month']==m) & (df_tr_2014['day_m']==d)]['20d']
df_te.loc[i,'7d'] = float(dk_7)
df_te.loc[i,'15d'] = float(dk_15)
df_te.loc[i,'20d'] = float(dk_20)
EDIT:
Sample data:
np.random.seed(10)
N = 365
rng = pd.date_range('2014-01-01', periods=N)
df_tr_2014 = pd.DataFrame(np.random.randint(10, size=(N, 3)), index=rng).reset_index()
df_tr_2014.columns = ['datetime','7d','15d','20d']
df_tr_2014.insert(1,'month', df_tr_2014['datetime'].dt.month)
df_tr_2014.insert(2,'day_m', df_tr_2014['datetime'].dt.day)
#print (df_tr_2014.head())
N = 365
rng = pd.date_range('2015-01-01', periods=N)
df_te = pd.DataFrame(index=rng)
df_te['month'] = df_te.index.month
df_te['day_m'] = df_te.index.day
df_te = df_te.reset_index()
#print (df_te.head())
I have the following Data Frame named: mydf:
A B
0 3de (1ABS) Adiran
1 3SA (SDAS) Adel
2 7A (ASA) Ronni
3 820 (SAAa) Emili
I want to remove the " (xxxx)" and keeps the values in column A , so the dataframe (mydf) will look like:
A B
0 3de Adiran
1 3SA Adel
2 7A Ronni
3 820 Emili
I have tried :
print mydf['A'].apply(lambda x: re.sub(r" \(.+\)", "", x) )
but then I get a Series object back and not a dataframe object.
I have also tried to use replace:
df.replace([' \(.*\)'],[""], regex=True), But it didn't change anything.
What am I doing wrong?
Thank you!
you can use str.split() method:
In [3]: df.A = df.A.str.split('\s+\(').str[0]
In [4]: df
Out[4]:
A B
0 3de Adiran
1 3SA Adel
2 7A Ronni
3 820 Emili
or using str.extract() method:
In [9]: df.A = df.A.str.extract(r'([^\(\s]*)', expand=False)
In [10]: df
Out[10]:
A B
0 3de Adiran
1 3SA Adel
2 7A Ronni
3 820 Emili