I am dealing with a dataframe called "Data" containing two columns, one is the userid, the other one a datetime object:
userid | eventTime
1 | 2018-11-01 07:36:58
2 | 2018-11-07 08:04:12
.. | ....
My goal is now to replace the entry in this dataframe column eventTime with just the number of the day in the specific month as integer value, e.g. the first day (=1) in November and the seventh day (=7) in November.
So the Result shall be
userid | eventTime
1 | 1
2 | 7
.. | ....
How can I get this done?
I already extracted indices from the data frame and tried to modify it in a loop but I don't know how it shall work:
temp = Data.index.get_values() #get indices from data frame
for temp, row in Data.iterrows():
print(row['eventTime'])
df['eventTime'] = df['eventTime'].dt.day
Should work - assuming df is your dataframe
Performing a vector operation like this is the most efficient way to work on a dataframe
As an example:
df = pd.DataFrame(data={'user': ['a', 'b', 'a', 'b'],
'eventTime': [datetime(2000, 1, 1),
datetime(2000, 2, 2),
datetime(2000, 3, 3),
datetime(2000, 4, 4)]})
print(df)
# eventTime user
# 0 2000-01-01 a
# 1 2000-02-02 b
# 2 2000-03-03 a
# 3 2000-04-04 b
You can operate on a particular column with the apply() method, and datetime objects provide a datetime.day property that gives you the day of the month as an integer:
df['day'] = df.eventTime.apply(lambda x: x.day)
print(df)
# eventTime user day
# 0 2000-01-01 a 1
# 1 2000-02-02 b 2
# 2 2000-03-03 a 3
# 3 2000-04-04 b 4
If you want to replace the eventTime column instead of create a new column, just use:
df['eventTime'] = df.eventTime.apply(lambda x: x.day)
Related
I want to merge several strings in a dataframe based on a groupedby in Pandas.
This is my code so far:
import pandas as pd
from io import StringIO
data = StringIO("""
"name1","hej","2014-11-01"
"name1","du","2014-11-02"
"name1","aj","2014-12-01"
"name1","oj","2014-12-02"
"name2","fin","2014-11-01"
"name2","katt","2014-11-02"
"name2","mycket","2014-12-01"
"name2","lite","2014-12-01"
""")
# load string as stream into dataframe
df = pd.read_csv(data,header=0, names=["name","text","date"],parse_dates=[2])
# add column with month
df["month"] = df["date"].apply(lambda x: x.month)
I want the end result to look like this:
I don't get how I can use groupby and apply some sort of concatenation of the strings in the column "text". Any help appreciated!
You can groupby the 'name' and 'month' columns, then call transform which will return data aligned to the original df and apply a lambda where we join the text entries:
In [119]:
df['text'] = df[['name','text','month']].groupby(['name','month'])['text'].transform(lambda x: ','.join(x))
df[['name','text','month']].drop_duplicates()
Out[119]:
name text month
0 name1 hej,du 11
2 name1 aj,oj 12
4 name2 fin,katt 11
6 name2 mycket,lite 12
I sub the original df by passing a list of the columns of interest df[['name','text','month']] here and then call drop_duplicates
EDIT actually I can just call apply and then reset_index:
In [124]:
df.groupby(['name','month'])['text'].apply(lambda x: ','.join(x)).reset_index()
Out[124]:
name month text
0 name1 11 hej,du
1 name1 12 aj,oj
2 name2 11 fin,katt
3 name2 12 mycket,lite
update
the lambda is unnecessary here:
In[38]:
df.groupby(['name','month'])['text'].apply(','.join).reset_index()
Out[38]:
name month text
0 name1 11 du
1 name1 12 aj,oj
2 name2 11 fin,katt
3 name2 12 mycket,lite
We can groupby the 'name' and 'month' columns, then call agg() functions of Panda’s DataFrame objects.
The aggregation functionality provided by the agg() function allows multiple statistics to be calculated per group in one calculation.
df.groupby(['name', 'month'], as_index = False).agg({'text': ' '.join})
The answer by EdChum provides you with a lot of flexibility but if you just want to concateate strings into a column of list objects you can also:
output_series = df.groupby(['name','month'])['text'].apply(list)
If you want to concatenate your "text" in a list:
df.groupby(['name', 'month'], as_index = False).agg({'text': list})
For me the above solutions were close but added some unwanted /n's and dtype:object, so here's a modified version:
df.groupby(['name', 'month'])['text'].apply(lambda text: ''.join(text.to_string(index=False))).str.replace('(\\n)', '').reset_index()
Please try this line of code : -
df.groupby(['name','month'])['text'].apply(','.join).reset_index()
Although, this is an old question. But just in case. I used the below code and it seems to work like a charm.
text = ''.join(df[df['date'].dt.month==8]['text'])
Thanks to all the other answers, the following is probably the most concise and feels more natural. Using df.groupby("X")["A"].agg() aggregates over one or many selected columns.
df = pandas.DataFrame({'A' : ['a', 'a', 'b', 'c', 'c'],
'B' : ['i', 'j', 'k', 'i', 'j'],
'X' : [1, 2, 2, 1, 3]})
A B X
a i 1
a j 2
b k 2
c i 1
c j 3
df.groupby("X", as_index=False)["A"].agg(' '.join)
X A
1 a c
2 a b
3 c
df.groupby("X", as_index=False)[["A", "B"]].agg(' '.join)
X A B
1 a c i i
2 a b j k
3 c j
I want to merge several strings in a dataframe based on a groupedby in Pandas.
This is my code so far:
import pandas as pd
from io import StringIO
data = StringIO("""
"name1","hej","2014-11-01"
"name1","du","2014-11-02"
"name1","aj","2014-12-01"
"name1","oj","2014-12-02"
"name2","fin","2014-11-01"
"name2","katt","2014-11-02"
"name2","mycket","2014-12-01"
"name2","lite","2014-12-01"
""")
# load string as stream into dataframe
df = pd.read_csv(data,header=0, names=["name","text","date"],parse_dates=[2])
# add column with month
df["month"] = df["date"].apply(lambda x: x.month)
I want the end result to look like this:
I don't get how I can use groupby and apply some sort of concatenation of the strings in the column "text". Any help appreciated!
You can groupby the 'name' and 'month' columns, then call transform which will return data aligned to the original df and apply a lambda where we join the text entries:
In [119]:
df['text'] = df[['name','text','month']].groupby(['name','month'])['text'].transform(lambda x: ','.join(x))
df[['name','text','month']].drop_duplicates()
Out[119]:
name text month
0 name1 hej,du 11
2 name1 aj,oj 12
4 name2 fin,katt 11
6 name2 mycket,lite 12
I sub the original df by passing a list of the columns of interest df[['name','text','month']] here and then call drop_duplicates
EDIT actually I can just call apply and then reset_index:
In [124]:
df.groupby(['name','month'])['text'].apply(lambda x: ','.join(x)).reset_index()
Out[124]:
name month text
0 name1 11 hej,du
1 name1 12 aj,oj
2 name2 11 fin,katt
3 name2 12 mycket,lite
update
the lambda is unnecessary here:
In[38]:
df.groupby(['name','month'])['text'].apply(','.join).reset_index()
Out[38]:
name month text
0 name1 11 du
1 name1 12 aj,oj
2 name2 11 fin,katt
3 name2 12 mycket,lite
We can groupby the 'name' and 'month' columns, then call agg() functions of Panda’s DataFrame objects.
The aggregation functionality provided by the agg() function allows multiple statistics to be calculated per group in one calculation.
df.groupby(['name', 'month'], as_index = False).agg({'text': ' '.join})
The answer by EdChum provides you with a lot of flexibility but if you just want to concateate strings into a column of list objects you can also:
output_series = df.groupby(['name','month'])['text'].apply(list)
If you want to concatenate your "text" in a list:
df.groupby(['name', 'month'], as_index = False).agg({'text': list})
For me the above solutions were close but added some unwanted /n's and dtype:object, so here's a modified version:
df.groupby(['name', 'month'])['text'].apply(lambda text: ''.join(text.to_string(index=False))).str.replace('(\\n)', '').reset_index()
Please try this line of code : -
df.groupby(['name','month'])['text'].apply(','.join).reset_index()
Although, this is an old question. But just in case. I used the below code and it seems to work like a charm.
text = ''.join(df[df['date'].dt.month==8]['text'])
Thanks to all the other answers, the following is probably the most concise and feels more natural. Using df.groupby("X")["A"].agg() aggregates over one or many selected columns.
df = pandas.DataFrame({'A' : ['a', 'a', 'b', 'c', 'c'],
'B' : ['i', 'j', 'k', 'i', 'j'],
'X' : [1, 2, 2, 1, 3]})
A B X
a i 1
a j 2
b k 2
c i 1
c j 3
df.groupby("X", as_index=False)["A"].agg(' '.join)
X A
1 a c
2 a b
3 c
df.groupby("X", as_index=False)[["A", "B"]].agg(' '.join)
X A B
1 a c i i
2 a b j k
3 c j
I have several dataframes indexed more or less by the same MultiIndex (a few values may be missing on each dataframe, but the total rows exceeds 70K and the missing values is always less than 10). I want to attach/merge/concatenate to all of them a given dataframe (with same indexation). I tried doing this using a for iteration with a tuple, as in the example here. However, at the end, all my data frames do not merge. I provide a simple example where this happens. Why they do not merge?
df1 = pd.DataFrame(np.arange(12).reshape(4,3), index = ["A", "B", "C", "D"], columns = ["1st", "2nd", "3rd"])
df2 = df1 + 2
df3 = df1 - 2
for df in (df1, df2):
df = pd.merge(df, df3, left_index = True, right_index = True, how = "inner")
df1, df2
What is your expected result?
In the for loop, df is the loop variable and also the result on the left-hand side of the assignment statement. Here is the same loop with print statements to provide additional information. I think you are over-writing intermediate results.
for df in (df1, df2):
print(df)
print('-----')
df = pd.merge(df, df3, left_index = True, right_index = True, how = "inner")
print(df)
print('==========', end='\n\n')
print(df)
You could combine df1, df2 and df3 like this.
print(pd.concat([df1, df2, df3], axis=1))
1st 2nd 3rd 1st 2nd 3rd 1st 2nd 3rd
A 0 1 2 2 3 4 -2 -1 0
B 3 4 5 5 6 7 1 2 3
C 6 7 8 8 9 10 4 5 6
D 9 10 11 11 12 13 7 8 9
UPDATE
Here is an idiomatic way to import and concatenate several CSV files, possibly in multiple directories. In short: read each file into a separate data frame; add each data frame to a list; concatenate once at the end.
Reference: https://pandas.pydata.org/docs/user_guide/cookbook.html#reading-multiple-files-to-create-a-single-dataframe
import pandas as pd
from pathlib import Path
df = list()
for filename in Path.cwd().rglob('*.csv'):
with open(filename, 'rt') as handle:
t = pd.read_csv(handle)
df.append(t)
print(filename.name, t.shape)
df = pd.concat(df)
print('\nfinal: ', df.shape)
penny.csv (62, 8)
penny-2020-06-24.csv (144, 9)
...etc
final: (474, 20)
I have two columns in a data frame. I want to combine those columns into a single column.
df = pd.DataFrame({'a': [500, 200, 13, 47], 'b':['$', '€', .586,.02]})
df
Out:
a b
0 500 $
1 200 €
2 13 .586
3 47 .02
I want to merge that two columns without affecting the data.
Expected output:
df
Out:
a
0 500$
1 200€
2 13.586
3 47.02
Please help me with this...
I tried the below solution, but it does not work for me,
df.b=np.where(df.b,df.b,df.a)
df.loc[df['b'] == '', 'b'] = df['a']
First solution working with convert both columns to strings and then join with +, last convert Series to one column DataFrame - but it working only if numbers less like 1 for column b:
df1 = df.astype(str)
df = (df1.a + df1.b.str.replace(r'^0', '')).to_frame('a')
print (df)
a
0 500$
1 200€
2 13.586
3 47.02
Or if want mixed values numeric for last 2 rows and strings for first 2 rows use:
m = df.b.apply(lambda x: isinstance(x, str))
df.loc[m, 'a'] = df.loc[m, 'a'].astype(str) + df.b
df.loc[~m, 'a'] += df.pop('b')
print (df)
a
0 500$
1 200€
2 13.586
3 47.02
I have 2 pandas dataframes, one of them contains dates with measurements, and the other contains dates with an event ID.
df1
from datetime import datetime as dt
from datetime import timedelta
import pandas as pd
import numpy as np
today = dt.now()
ndays = 10
df1 = pd.DataFrame({'Date': [today + timedelta(days = x) for x in range(ndays)], 'measurement': pd.Series(np.random.randint(1, high = 10, size = ndays))})
df1.Date = df1.Date.dt.date
Date measurement
2018-01-10 8
2018-01-11 2
2018-01-12 7
2018-01-13 3
2018-01-14 1
2018-01-15 1
2018-01-16 6
2018-01-17 9
2018-01-18 8
2018-01-19 4
df2
df2 = pd.DataFrame({'Date': ['2018-01-11', '2018-01-14', '2018-01-16', '2018-01-19'], 'letter': ['event_a', 'event_b', 'event_c', 'event_d']})
df2.Date = pd.to_datetime(df2.Date, format = '%Y-%m-%d')
df2.Date = df2.Date.dt.date
Date event_id
2018-01-11 event_a
2018-01-14 event_b
2018-01-16 event_c
2018-01-19 event_d
I give the dates in df1 an event_id from df2 only if it's between two event dates. The resulting dataframe would look something like:
df3
today = dt.now()
ndays = 10
df3 = pd.DataFrame({'Date': [today + timedelta(days = x) for x in range(ndays)], 'measurement': pd.Series(np.random.randint(1, high = 10, size = ndays)), 'event_id': ['event_a', 'event_a', 'event_b', 'event_b', 'event_b', 'event_c', 'event_c', 'event_d', 'event_d', 'event_d']})
df3.Date = df3.Date.dt.date
Date event_id measurement
2018-01-10 event_a 4
2018-01-11 event_a 2
2018-01-12 event_b 1
2018-01-13 event_b 5
2018-01-14 event_b 5
2018-01-15 event_c 4
2018-01-16 event_c 6
2018-01-17 event_d 6
2018-01-18 event_d 9
2018-01-19 event_d 6
The code I use to achieve this is:
n = 1
while n <= len(list(df2.Date)) - 1 :
for date in list(df1.Date):
if date <= df2.iloc[n].Date and (date > df2.iloc[n-1].Date):
df1.loc[df1.Date == date, 'event_id'] = df2.iloc[n].event_id
n += 1
The dataset that I am working with is significantly larger than this (a few million rows) and this method runs far too long. Is there a more efficient way to accomplish this?
So there are quite a few things to improve performance.
The first question I have is: does it have to be a pandas frame to begin with? Meaning can't df1 and df2 just be lists of tuples or list of lists?
The thing is that pandas adds a significant overhead when accessing items but especially when setting values individually.
Pandas excels when it comes to vectorized operations but I don't see an efficient alternative right now (maybe someone comes up with such an answer, that would be ideal).
Now what I'd do is:
Convert your df1 and df2 to records -> e.g. d1 = df1.to_records() what you get is an array of tuples, basically with the same structure as the dataframe.
Now run your algorithm but instead of operating on pandas dataframes you operate on the arrays of tuples d1 and d2
Use a third list of tuples d3 where you store the newly created data (each tuple is a row)
Now if you want you can convert d3 back to a pandas dataframe:
df3 = pd.DataFrame.from_records(d3, myKwArgs**)
This will speed up your code significantly I'd assume by more than 100-1000%. It does increase memory usage though, so if you are low on memory try to avoid the pandas dataframes all-together or dereference unused pandas frames df1, df2 once you used them to create the records (and if you run into problems call gc manually).
EDIT: Here a version of your code using the procedure above:
d3 = []
n = 1
while n < range(len(d2)):
for i in range(len(d1)):
date = d1[i][0]
if date <= d2[n][0] and date > d2[n-1][0]:
d3.append( (date, d2[n][1], d1[i][1]) )
n += 1
You can try df.apply() method to achieve this. Refer pandas.DataFrame.apply. I think my code will works faster than yours.
My approach:
Merge two dataframes df1 and df2 and create new one df3 by
df3 = pd.merge(df1, df2, on='Date', how='outer')
Sort df3 by date to make easy to travserse.
df3['Date'] = pd.to_datetime(df3.Date)
df3.sort_values(by='Date')
Create set_event_date() method to apply for each rows in df3.
new_event_id = np.nan
def set_event_date(df3):
global new_event_id
if df3.event_id is not np.nan:
new_event_id = df3.event_id
return new_event_id
Apply set_event_method() to each rows in df3.
df3['new_event_id'] = df3.apply(set_event_date,axis=1)
Final Output will be:
Date Measurement New_event_id
0 2018-01-11 2 event_a
1 2018-01-12 1 event_a
2 2018-01-13 3 event_a
3 2018-01-14 6 event_b
4 2018-01-15 3 event_b
5 2018-01-16 5 event_c
6 2018-01-17 7 event_c
7 2018-01-18 9 event_c
8 2018-01-19 7 event_d
9 2018-01-20 4 event_d
Let me know once you tried my solution and it works faster than yours.
Thanks.