I have a pandas dataframe df looking like this:
a b
thisisastring 5
anotherstring 6
thirdstring 7
I want to remove characters from the left of the strings in column a based on the number in column b. So I tried:
df["a"] = d["a"].str[df["b"]:]
But this will result in:
a b
NaN 5
NaN 6
NaN 7
Instead of:
a b
sastring 5
rstring 6
ring 7
Any help? Thanks in advance!
Using zip with string slice
df.a=[x[y:] for x,y in zip(df.a,df.b)]
df
Out[584]:
a b
0 sastring 5
1 rstring 6
2 ring 7
You can do it with apply, to apply this row-wise:
df.apply(lambda x: x.a[x.b:],axis=1)
0 sastring
1 rstring
2 ring
dtype: object
Related
I have 2 dataframes as below
import pandas as pd
dat = pd.DataFrame({'val1' : [1,2,1,2,4], 'val2' : [1,2,1,2,4]})
dat1 = pd.DataFrame({'val3' : [1,2,1,2,4]})
Now with each column of dat and want to multiply dat1. So I did below
dat * dat1
However this generates nan value for all elements.
Could you please help on what is the correct approach? I could run a for loop with each column of dat, but I wonder if there are any better method available to perform the same.
Thanks for your pointer.
When doing multiplication (or any arithmetic operation), pandas does index alignment. This goes for both the index and columns in case of dataframes. If matches, it multiplies; otherwise puts NaN and the result has the union of the indices and columns of the operands.
So, to "avoid" this alignment, make dat1 a label-unaware data structure, e.g., a NumPy array:
In [116]: dat * dat1.to_numpy()
Out[116]:
val1 val2
0 1 1
1 4 4
2 1 1
3 4 4
4 16 16
To see what's "really" being multiplied, you can align yourself:
In [117]: dat.align(dat1)
Out[117]:
( val1 val2 val3
0 1 1 NaN
1 2 2 NaN
2 1 1 NaN
3 2 2 NaN
4 4 4 NaN,
val1 val2 val3
0 NaN NaN 1
1 NaN NaN 2
2 NaN NaN 1
3 NaN NaN 2
4 NaN NaN 4)
(extra: you have the indices same for dat & dat1; please change one of them's index, and then align again to see the union-behaviour.)
You need to change two things:
use mul with axis=0
use a Series instead of dat1 (else multiplication will try to align the indices, there is no common ones between your two dataframes
out = dat.mul(dat1['val3'], axis=0)
output:
val1 val2
0 1 1
1 4 4
2 1 1
3 4 4
4 16 16
I need to change the format for values in a column in a dataframe. If I have a dataframe in that format:
df =
sector funding_total_usd
1 NaN
2 10,00,000
3 3,90,000
4 34,06,159
5 2,17,50,000
6 20,00,000
How to change it to that format:
df =
sector funding_total_usd
1 NaN
2 10000.00
3 3900.00
4 34061.59
5 217500.00
6 20000.00
This is my code:
for row in df['funding_total_usd']:
dt1 = row.replace (',','')
print (dt1)
This is the error that I got "AttributeError: 'float' object has no attribute 'replace'"
I need really to your help in how to do that?
Here's the way to get the decimal places:
import pandas as pd
import numpy as np
df= pd.DataFrame({'funding_total_usd': [np.nan, 1000000, 390000, 3406159,21750000,2000000]})
print(df)
df['funding_total_usd'] /= 100
print(df)
funding_total_usd
0 NaN
1 1000000.0
2 390000.0
3 3406159.0
4 21750000.0
funding_total_usd
0 NaN
1 10000.00
2 3900.00
3 34061.59
4 217500.00
To solve your comma problem, please run this as your first command before you print. It will remove all your commas for the float values.
pd.options.display.float_format = '{:.2f}'.format
I have a dataframe like this:
A B C
0 1 0.749065 This
1 2 0.301084 is
2 3 0.463468 a
3 4 0.643961 random
4 1 0.866521 string
5 2 0.120737 !
Calling
In [10]: print df.groupby("A")["B"].sum()
will return
A
1 1.615586
2 0.421821
3 0.463468
4 0.643961
Now I would like to do "the same" for column "C". Because that column contains strings, sum() doesn't work (although you might think that it would concatenate the strings). What I would really like to see is a list or set of the strings for each group, i.e.
A
1 {This, string}
2 {is, !}
3 {a}
4 {random}
I have been trying to find ways to do this.
Series.unique() (http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.unique.html) doesn't work, although
df.groupby("A")["B"]
is a
pandas.core.groupby.SeriesGroupBy object
so I was hoping any Series method would work. Any ideas?
In [4]: df = read_csv(StringIO(data),sep='\s+')
In [5]: df
Out[5]:
A B C
0 1 0.749065 This
1 2 0.301084 is
2 3 0.463468 a
3 4 0.643961 random
4 1 0.866521 string
5 2 0.120737 !
In [6]: df.dtypes
Out[6]:
A int64
B float64
C object
dtype: object
When you apply your own function, there is not automatic exclusions of non-numeric columns. This is slower, though, than the application of .sum() to the groupby
In [8]: df.groupby('A').apply(lambda x: x.sum())
Out[8]:
A B C
A
1 2 1.615586 Thisstring
2 4 0.421821 is!
3 3 0.463468 a
4 4 0.643961 random
sum by default concatenates
In [9]: df.groupby('A')['C'].apply(lambda x: x.sum())
Out[9]:
A
1 Thisstring
2 is!
3 a
4 random
dtype: object
You can do pretty much what you want
In [11]: df.groupby('A')['C'].apply(lambda x: "{%s}" % ', '.join(x))
Out[11]:
A
1 {This, string}
2 {is, !}
3 {a}
4 {random}
dtype: object
Doing this on a whole frame, one group at a time. Key is to return a Series
def f(x):
return Series(dict(A = x['A'].sum(),
B = x['B'].sum(),
C = "{%s}" % ', '.join(x['C'])))
In [14]: df.groupby('A').apply(f)
Out[14]:
A B C
A
1 2 1.615586 {This, string}
2 4 0.421821 {is, !}
3 3 0.463468 {a}
4 4 0.643961 {random}
You can use the apply method to apply an arbitrary function to the grouped data. So if you want a set, apply set. If you want a list, apply list.
>>> d
A B
0 1 This
1 2 is
2 3 a
3 4 random
4 1 string
5 2 !
>>> d.groupby('A')['B'].apply(list)
A
1 [This, string]
2 [is, !]
3 [a]
4 [random]
dtype: object
If you want something else, just write a function that does what you want and then apply that.
You may be able to use the aggregate (or agg) function to concatenate the values. (Untested code)
df.groupby('A')['B'].agg(lambda col: ''.join(col))
You could try this:
df.groupby('A').agg({'B':'sum','C':'-'.join})
Named aggregations with pandas >= 0.25.0
Since pandas version 0.25.0 we have named aggregations where we can groupby, aggregate and at the same time assign new names to our columns. This way we won't get the MultiIndex columns, and the column names make more sense given the data they contain:
aggregate and get a list of strings
grp = df.groupby('A').agg(B_sum=('B','sum'),
C=('C', list)).reset_index()
print(grp)
A B_sum C
0 1 1.615586 [This, string]
1 2 0.421821 [is, !]
2 3 0.463468 [a]
3 4 0.643961 [random]
aggregate and join the strings
grp = df.groupby('A').agg(B_sum=('B','sum'),
C=('C', ', '.join)).reset_index()
print(grp)
A B_sum C
0 1 1.615586 This, string
1 2 0.421821 is, !
2 3 0.463468 a
3 4 0.643961 random
a simple solution would be :
>>> df.groupby(['A','B']).c.unique().reset_index()
If you'd like to overwrite column B in the dataframe, this should work:
df = df.groupby('A',as_index=False).agg(lambda x:'\n'.join(x))
Following #Erfan's good answer, most of the times in an analysis of aggregate values you want the unique possible combinations of these existing character values:
unique_chars = lambda x: ', '.join(x.unique())
(df
.groupby(['A'])
.agg({'C': unique_chars}))
I have a dataframe that contains nan values in particular column. while iterating through the rows, if it come across nan(using isnan() method) then I need to change it to some other value(since I have some conditions). I tried using replace() and fillna() with limit parameter also but they are modifying whole column when they come across the first nan value? Is there any method that I can assign value to specific nan rather than changing all the values of a column?
Example: the dataframe looks like it:
points sundar cate king varun vicky john charlie target_class
1 x2 5 'cat' 4 10 3 2 1 NaN
2 x3 3 'cat' 1 2 3 1 1 NaN
3 x4 6 'lion' 8 4 3 7 1 NaN
4 x5 4 'lion' 1 1 3 1 1 NaN
5 x6 8 'cat' 10 10 9 7 1 0.0
an I have a list like
a = [1.0, 0.0]
and I expect to be like
points sundar cate king varun vicky john charlie target_class
1 x2 5 'cat' 4 10 3 2 1 1.0
2 x3 3 'cat' 1 2 3 1 1 1.0
3 x4 6 'lion' 8 4 3 7 1 1.0
4 x5 4 'lion' 1 1 3 1 1 0.0
5 x6 8 'cat' 10 10 9 7 1 0.0
I wanted to change the target_class values based on some conditions and assign values of the above list.
I believe need replace NaNs values to 1 only for indexes specified in list idx:
mask = df['target_class'].isnull()
idx = [1,2,3]
df.loc[mask, 'target_class'] = df[mask].index.isin(idx).astype(int)
print (df)
points sundar cate king varun vicky john charlie target_class
1 x2 5 'cat' 4 10 3 2 1 1.0
2 x3 3 'cat' 1 2 3 1 1 1.0
3 x4 6 'lion' 8 4 3 7 1 1.0
4 x5 4 'lion' 1 1 3 1 1 0.0
5 x6 8 'cat' 10 10 9 7 1 0.0
Or:
idx = [1,2,3]
s = pd.Series(df.index.isin(idx).astype(int), index=df.index)
df['target_class'] = df['target_class'].fillna(s)
EDIT:
From comments solution is assign values by index and columns values with DataFrame.loc:
df2.loc['x2', 'target_class'] = list1[0]
I suppose your conditions for imputing the nan values does not depend on the number of them in a column. In the code below I stored all the imputation rules in one function that receives as parameters the entire row (containing the nan) and the column you are investigating for. If you also need all the dataframe for the imputation rules, just pass it through the replace_nan function. In the example I imputate the col element with the mean values of the other columns.
import pandas as pd
import numpy as np
def replace_nan(row, col):
row[col] = row.drop(col).mean()
return row
df = pd.DataFrame(np.random.rand(5,3), columns = ['col1', 'col2', 'col3'])
col_to_impute = 'col1'
df.loc[[1, 3], col_to_impute] = np.nan
df = df.apply(lambda x: replace_nan(x, col_to_impute) if np.isnan(x[col_to_impute]) else x, axis=1)
The only thing that you should do is making the right assignation. That is, make an assignation in the rows that contain nulls.
Example dataset:
,event_id,type,timestamp,label
0,asd12e,click,12322232,0.0
1,asj123,click,212312312,0.0
2,asd321,touch,12312323,0.0
3,asdas3,click,33332233,
4,sdsaa3,touch,33211333,
Note: The last two rows contains nulls in column: 'label'. Then, we load the dataset:
df = pd.read_csv('dataset.csv')
Now, we make the appropiate condition:
cond = df['label'].isnull()
Now, we make the assignation over these rows (I don't know the logical of assignation. Therefore I assign 1 value to NaN's):
df1.loc[cond,'label'] = 1
There are another more accurate approaches. fillna() method could be used. You should provide the logical in order to help you.
I am brand new to Python and stacks exchange. I have been trying to replace invalid values ( x<-3 and x>12) with np.nan in specific columns.
I don't know how many columns I will have to deal with and thus will have to create a general code that takes this into account. I do however know, that the first two columns are ids and names respectively. I have searched google and stacks exchange for a solution but haven't been able to find a solution that solves my specific objective.
My question is; How would one replace values found in the third column and onwards?
My dataframe looks like this;
Data
I tried this line:
Data[Data > 12.0] = np.nan.
this replaced the first two columns with nan
1st attempt
I tried this line:
Data[(Data.iloc[(range(2,Columns))] >=12) & (Data.iloc[(range(2,Columns))]<=-3)] = np.nan
where,
Columns = len(Data.columns)
This is clearly wrong replacing all values in rows 2 to 6 (Columns = 7).
2nd attempt
Any thoughts would be greatly appreciated.
Python 3.6.1 64bits, Qt 5.6.2, PyQt5 5.6 on Darwin
You're looking for the applymap() method.
import pandas as pd
import numpy as np
# get the columns after the second one
cols = Data.columns[2:]
# apply mask to those columns
new_df = Data[cols].applymap(lambda x: np.nan if x > 12 or x <= -3 else x)
Documentation: https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.applymap.html
This approach assumes your columns after the second contain float or int values.
You can set values to specific columns of a dataframe by using iloc and slicing the columns that you need. Then we can set the values using where
A short example using some random data
df = pd.DataFrame(np.random.randint(0,10,(4,10)))
0 1 2 3 4 5 6 7 8 9
0 7 7 9 4 2 6 6 1 7 9
1 0 1 2 4 5 5 3 9 0 7
2 0 1 4 4 3 8 7 0 6 1
3 1 4 0 2 5 7 2 7 9 9
Now we set the region to update and the region we want to update using iloc, and we slice columns indexed as 2 to the last column
df.iloc[:,2:] = df.iloc[:,2:].where((df < 7) & (df > 2))
Which will set the values in the Data Frame to NaN.
0 1 2 3 4 5 6 7 8 9
0 7 7 NaN 4.0 NaN 6.0 6.0 NaN NaN NaN
1 0 1 NaN 4.0 5.0 5.0 3.0 NaN NaN NaN
2 0 1 4.0 4.0 3.0 NaN NaN NaN 6.0 NaN
3 1 4 NaN NaN 5.0 NaN NaN NaN NaN NaN
For your data the code would be this
Data.iloc[:,2:] = Data.iloc[:,2:].where((Data <= 12) & (Data >= -3))
Operator clarification
The setup I show directly above would look like this
-3 <= Data <= 12, gives everything between those numbers
If we reverse this logic using the & operator it looks like this
-3 >= Data <= 12, a number cannot be both less than -3 and greater than 12 at the same time.
So we use the or operator instead |. Code looks like this now....
Data.iloc[:,2:] = Data.iloc[:,2:].where((Data >= 12) | (Data <= -3))
So the data is checked on a conditional basis
Data <= -3 or Data >= 12