having two columns A and B in a dataframe:
A B
0 1 6
1 2 7
2 1 8
3 2 9
4 1 10
I would like to create a column C. C must have values of B shifted by value of A:
A B C
0 1 6 NaN
1 2 7 NaN
2 1 8 7
3 2 9 7
4 1 10 9
The command:
df['C'] = df['B'].shift(df['A'])
does not work.
Do you have any other ideas?
I'd use help from numpy to avoid the apply
l = np.arange(len(df)) - df.A.values
df['C'] = np.where(l >=0, df.B.values[l], np.nan)
df
A B C
0 1 6 NaN
1 2 7 NaN
2 1 8 7.0
3 2 9 7.0
4 1 10 9.0
simple time test
This is tricky due to index alignment, you can define a user func and apply row-wise on your df, here the function will perform a shift on the B column and return the index value (using .name attribute to return the index) of the shifted column:
In [134]:
def func(x):
return df['B'].shift(x['A'])[x.name]
df['C'] = df.apply(lambda x: func(x), axis=1)
df
Out[134]:
A B C
0 1 6 NaN
1 2 7 NaN
2 1 8 7.0
3 2 9 7.0
4 1 10 9.0
Related
I am looking forward to remove/drop all rows after first occurrence of NaN based on any of dataFrame column.
I have created two sample DataFrames as illustrated Below, the first dataframe the dtypes are for initial two columns are object while the last one in int, while in the Second dataframe these are float, obj and int.
First:
>>> df = pd.DataFrame({"A": (1,2,3,4,5,6,7,'NaN','NaN','NaN','NaN'),"B": (1,2,3,'NaN',4,5,6,7,'NaN',"9","10"),"C": range(11)})
>>> df
A B C
0 1 1 0
1 2 2 1
2 3 3 2
3 4 NaN 3
4 5 4 4
5 6 5 5
6 7 6 6
7 NaN 7 7
8 NaN NaN 8
9 NaN 9 9
10 NaN 10 10
Dtypes:
>>> df.dtypes
A object
B object
C int64
dtype: object
While carrying out index based approach as follows based on a particular, it works Just fine as long as dtype is obj and int but i'm looking for dataFrame level action merely not limited to a column.
>>> df[:df[df['A'] == 'NaN'].index[0]]
A B C
0 1 1 0
1 2 2 1
2 3 3 2
3 4 NaN 3
4 5 4 4
5 6 5 5
6 7 6 6
>>> df[:df[df['B'] == 'NaN'].index[0]]
A B C
0 1 1 0
1 2 2 1
2 3 3 2
Second:
Another interesting fact while creating DataFrame with np.nan where we get different dtype, then even index based approach failed for a single column operation s well.
>>> df = pd.DataFrame({"A": (1,2,3,4,5,6,7,np.nan,np.nan,np.nan,np.nan),"B": (1,2,3,np.nan,4,5,6,7,np.nan,"9","10"),"C": range(11)})
>>> df
A B C
0 1.0 1 0
1 2.0 2 1
2 3.0 3 2
3 4.0 NaN 3
4 5.0 4 4
5 6.0 5 5
6 7.0 6 6
7 NaN 7 7
8 NaN NaN 8
9 NaN 9 9
10 NaN 10 10
dtypes:
>>> df.dtypes
A float64
B object
C int64
dtype: object
Error:
>>> df[:df[df['B'] == 'NaN'].index[0]]
IndexError: index 0 is out of bounds for axis 0 with size 0
>>> df[:df[df['A'] == 'NaN'].index[0]]
IndexError: index 0 is out of bounds for axis 0 with size 0
Expected should be for the Second DataFrame:
>>> df
A B C
0 1.0 1 0
1 2.0 2 1
2 3.0 3 2
So, i am looking a way around to check across the entire DataFrame regardless of dtype and drop all rows from the first occurrence of NaN in the DataFrame.
You can try:
out=df.iloc[:df.isna().any(1).idxmax()]
OR
via replace() make your string 'NaN's to real 'NaN's then check for missing values and filter rows:
df=df.replace({'NaN':float('NaN'),'nan':float('NaN')})
out=df.iloc[:df.isna().any(1).idxmax()]
output of out:
A B C
0 1.0 1 0
1 2.0 2 1
2 3.0 3 2
Just for posterity ...
>>> df.iloc[:df.isna().any(1).argmax()]
A B C
0 1.0 1 0
1 2.0 2 1
2 3.0 3 2
>>> df.iloc[:df.isnull().any(1).argmax()]
A B C
0 1.0 1 0
1 2.0 2 1
2 3.0 3 2
I have a pandas data frame and I want to move the "F" column to after the "B" column. Is there a way to do that?
A B C D E F
0 7 1 8 1 6
1 8 2 5 8 5 8
2 9 3 6 8 5
3 1 8 1 3 4
4 6 8 2 5 0 9
5 2 N/A 1 3 8
df2
A B F C D E
0 7 1 6 8 1
1 8 2 8 5 8 5
2 9 3 5 6 8
3 1 4 8 1 3
4 6 8 9 2 5 0
5 2 8 N/A 1 3
So it should finally look like df2.
Thanks in advance.
You can try df.insert + df.pop after getting location of B by get_loc
df.insert(df.columns.get_loc("B")+1,"F",df.pop("F"))
print(df)
A B F C D E
0 7.0 1 6.0 NaN 8 1.0
1 8.0 2 8.0 5.0 8 5.0
2 9.0 3 5.0 6.0 8 NaN
3 1.0 8 NaN 1.0 3 4.0
4 6.0 8 9.0 2.0 5 0.0
5 NaN 2 8.0 NaN 1 3.0
Another minimalist, (and very specific!) approach:
df = df[list('ABFCDE')]
Here is a very simple answer to this(only one line). Giving littlebit more explanation to the answer from #warped
You can do that after you added the 'n' column into your df as follows.
import pandas as pd
df = pd.DataFrame({'l':['a','b','c','d'], 'v':[1,2,1,2]})
df['n'] = 0
df
l v n
0 a 1 0
1 b 2 0
2 c 1 0
3 d 2 0
# here you can add the below code and it should work.
df = df[list('nlv')]
df
n l v
0 0 a 1
1 0 b 2
2 0 c 1
3 0 d 2
However, if you have words in your columns names instead of letters. It should include two brackets around your column names.
import pandas as pd
df = pd.DataFrame({'Upper':['a','b','c','d'], 'Lower':[1,2,1,2]})
df['Net'] = 0
df['Mid'] = 2
df['Zsore'] = 2
df
Upper Lower Net Mid Zsore
0 a 1 0 2 2
1 b 2 0 2 2
2 c 1 0 2 2
3 d 2 0 2 2
# here you can add below line and it should work
df = df[list(('Mid','Upper', 'Lower', 'Net','Zsore'))]
df
Mid Upper Lower Net Zsore
0 2 a 1 0 2
1 2 b 2 0 2
2 2 c 1 0 2
3 2 d 2 0 2
I have the following dataframe:
a = pd.DataFrame([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]], columns=['a','b','c'])
a
Out[234]:
a b c
0 1 2 3
1 4 5 6
2 7 8 9
3 10 11 12
I want to add a column with only the last row as the mean of the last 2 values of column c. Something like:
a b c d
0 1 2 3 NaN
1 4 5 6 NaN
2 7 8 9 NaN
3 10 11 12 mean(9,12)
I tried this but the first part gives an error:
a['d'].iloc[-1] = a.c.iloc[-2:].values.mean()
You can use .at to assign at a single row/column label pair:
ix = a.shape[0]
a.at[ix-1,'d'] = a.loc[ix-2:ix, 'c'].values.mean()
a b c d
0 1 2 3 NaN
1 4 5 6 NaN
2 7 8 9 NaN
3 10 11 12 10.5
Also note that chained indexing (what you're doing with a.c.iloc[-2:]) is explicitly discouraged in the docs, given that pandas sees these operations as separate events, namely two separate calls to __getitem__, rather than a single call using a nested tuple of slices.
You may set d column beforehand (to ensure assignment):
In [100]: a['d'] = np.nan
In [101]: a['d'].iloc[-1] = a.c.iloc[-2:].mean()
In [102]: a
Out[102]:
a b c d
0 1 2 3 NaN
1 4 5 6 NaN
2 7 8 9 NaN
3 10 11 12 10.5
We can use .loc, .iloc & np.mean
a.loc[a.index.max(), 'd'] = np.mean(a.iloc[-2:, 2])
a b c d
0 1 2 3 NaN
1 4 5 6 NaN
2 7 8 9 NaN
3 10 11 12 10.5
Or just using .loc and np.mean:
a.loc[a.index.max(), 'd'] = np.mean(a.loc[a.index.max()-1:, 'c'])
a b c d
0 1 2 3 NaN
1 4 5 6 NaN
2 7 8 9 NaN
3 10 11 12 10.5
I have a row of pandas dataframe, i.e.
x p y q z
---------
1 4 2 5 3
I want to append only some columns ('x','y','z') of it to another dataframe as new columns with names 'a','b','c'.
Before:
A B
---
7 8
9 6
8 5
After
A B a b c
---------
7 8 1 2 3
9 6 1 2 3
8 5 1 2 3
try this,
df1=pd.DataFrame({'x':[1],'y':[2],'z':[3]})
df2=pd.DataFrame({'A':[7,9,8],'B':[8,6,5]})
print pd.concat([df2,df1],axis=1).fillna(method='ffill').rename(columns={'x':'a','y':'b','z':'c'})
A B a b c
0 7 8 1.0 2.0 3.0
1 9 6 1.0 2.0 3.0
2 8 5 1.0 2.0 3.0
Use assign by Series created by selecting 1. row of df1:
cols = ['x','y','z']
new_cols = ['a','b','c']
df = df2.assign(**pd.Series(df1[cols].iloc[0].values, index=new_cols))
print (df)
A B a b c
0 7 8 1 2 3
1 9 6 1 2 3
2 8 5 1 2 3
I have an existing pandas DataFrame, and I want to add a new column, where the value of each row will depend on the previous row.
for example:
df1 = pd.DataFrame(np.random.randint(10, size=(4, 4)), columns=['a', 'b', 'c', 'd'])
df1
Out[31]:
a b c d
0 9 3 3 0
1 3 9 5 1
2 1 7 5 6
3 8 0 1 7
and now I want to create column e, where for each row i the value of df1['e'][i] would be: df1['e'][i] = df1['d'][i] - df1['d'][i-1]
desired output:
df1:
a b c d e
0 9 3 3 0 0
1 3 9 5 1 1
2 1 7 5 6 5
3 8 0 1 7 1
how can I achieve this?
You can use sub with shift:
df['e'] = df.d.sub(df.d.shift(), fill_value=0)
print (df)
a b c d e
0 9 3 3 0 0.0
1 3 9 5 1 1.0
2 1 7 5 6 5.0
3 8 0 1 7 1.0
If need convert to int:
df['e'] = df.d.sub(df.d.shift(), fill_value=0).astype(int)
print (df)
a b c d e
0 9 3 3 0 0
1 3 9 5 1 1
2 1 7 5 6 5
3 8 0 1 7 1