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How to create a date range in python using pandas in Y-M-D?
import pandas as pd
df = pd.DataFrame([['2015-07-07','2016-09-22'],['2012-02-03','2013-02-19'],['2013-02-17','2013-03-22']],columns = ['start','end'])
#change strings to date format
df['start'] = [pd.to_datetime(x) for x in df['start']]
df['end'] = [pd.to_datetime(x) for x in df['end']]
df['range'] = df['end']-df['start']
df
Output should be:
start end range
0 2015-07-07 2016-09-22 443 days
1 2012-02-03 2013-02-19 382 days
2 2013-02-17 2013-03-22 33 days
In case you want to read from csv, switch the beginning to:
df = pd.read_csv('file_name.csv')
in case you want a concatenated column:
df['details'] = [str(x)+' - '+str(y)+' has '+str(z)[:-9] for x,y,z in zip(df['start'],df['end'],df['range'])]
Related
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I want to convert text file to csv file
import pandas as pd
readfile = pd.read_csv(r'text.txt')
readfile.to_csv(r'CSV.csv, index=None)
my text file format:
the result:
In the red circle it's add a decimal number follow the data it's duplicate
I don't want it to add a decimal number
please suggestion me what to do next, thank you.
and if it possible to read file and convert to csv with limit column please advise!
#just add header = None, since first line of txt is considered header that's why it is managing duplicate column names.
import pandas as pd
readfile = pd.read_csv(r'text.txt',header=None)
readfile.to_csv(r'CSV.csv, index=None)
#sample example output of readfile
0 1 2 3 4 5 6 7 8
0 1 2 3 5 0.0 0.0 0.0 4 6
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dframe= pd.DataFrame({'col1':['A']*3 + ['B']*4 + ['C','B','A'],'col2':[2,3,4,2,4,2,1,3,4,4]})
I want to remove duplicates from both columns and final result should look like this:
pd.DataFrame({'col1':['A'] + ['B'] + ['C'],'col2':[2,4,3]})
I tried following but the result was not as per the expectations
dframe.drop_duplicates(subset=['col1'], keep='first')
Please help.
Thanks
try:
via agg() and dropna() method:
out=dframe.agg(lambda x:pd.Series(pd.unique(x))).dropna()
OR
via apply() and dropna() method:
out=dframe.apply(lambda x:pd.Series(pd.unique(x))).dropna()
output of out:
col1 col2
0 A 2
1 B 3
2 C 4
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I have a Panda and want to do a calculation based on an existing column.
However, the apply. function is not working for some reason.
It's something like letssay
df = pd.DataFrame({'Age': age, 'Input': input})
and the input column is something like [1.10001, 1.49999, 1.60001]
Now I want to add a new column to the Dataframe, that is doing the following:
Add 0.0001 to each element in column
Multiply each value by 10
Transform each value of new column to int
Use Series.add, Series.mul and Series.astype:
#input is python code word (builtin), so better dont use it like variable
inp = [1.10001, 1.49999, 1.60001]
age = [10,20,30]
df = pd.DataFrame({'Age': age, 'Input': inp})
df['new'] = df['Input'].add(0.0001).mul(10).astype(int)
print (df)
Age Input new
0 10 1.10001 11
1 20 1.49999 15
2 30 1.60001 16
You could make a simple function and then apply it by row.
def f(row):
return int((row['input']+0.0001)*10))
df['new'] = df.apply(f, axis=1)
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My csv file looks like this:
5783,145v
g656,4589,3243,tt56
6579
How do I read this with pandas (or otherwise)?
(the table should contain empty cells)
You could pass a dummy separator, and then use str.split (by ",") with expand=True:
df = pd.read_csv('path/to/file.csv', sep=" ", header=None)
df = df[0].str.split(",", expand=True).fillna("")
print(df)
Output
0 1 2 3
0 5783 145v
1 g656 4589 3243 tt56
2 6579
I think that the solution proposed by #researchnewbie is good. If you need to replace the NaN values for say, zero, you could add this line after the read:
dataFrame.fillna(0, inplace=True)
Try doing the following:
import pandas as pd
dataFrame = pd.read_csv(filename)
Your empty cells should contain the NaN value, which essentially null.
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I have a dataframe df with 3 columns :
df=pd.DataFrame({
'User':['A','A','B','A','C','B','C'],
'Values':['x','y','z','p','q','r','s'],
'Date':[14,11,14,12,13,10,14]
})
I want to create a new dataframe that will contain the rows corresponding to highest values in the 'Date' columns for each user. For example for the above dataframe I want the desired dataframe to be as follows ( its a jpeg image):
Can anyone help me with this problem?
This answer assumes that there is different maximum values per user in Values column:
In [10]: def get_max(group):
...: return group[group.Date == group.Date.max()]
...:
In [12]: df.groupby('User').apply(get_max).reset_index(drop=True)
Out[12]:
Date User Values
0 14 A x
1 14 B z
2 14 C s