I got a pandas dataframe where two columns correspond to names of people. The columns are related and the same name means same person. I want to assign the category code such that it is valid for the whole "name" space.
For example my data frame is
df = pd.DataFrame({"P1":["a","b","c","a"], "P2":["b","c","d","c"]})
>>> df
P1 P2
0 a b
1 b c
2 c d
3 a c
I want it to be replaced by the corresponding category codes, such that
>>> df
P1 P2
0 1 2
1 2 3
2 3 4
3 1 3
The categories are in fact derived from the concatenated array ["a","b","c","d"] and applied on individual columns seperatly. How can I achive this ?.
Use:
print (df.stack().rank(method='dense').astype(int).unstack())
P1 P2
0 1 2
1 2 3
2 3 4
3 1 3
EDIT:
For more general solution I used another answer, because problem with duplicates in index:
df = pd.DataFrame({"P1":["a","b","c","a"],
"P2":["b","c","d","c"],
"A":[3,4,5,6]}, index=[2,2,3,3])
print (df)
A P1 P2
2 3 a b
2 4 b c
3 5 c d
3 6 a c
cols = ['P1','P2']
df[cols] = (pd.factorize(df[cols].values.ravel())[0]+1).reshape(-1, len(cols))
print (df)
A P1 P2
2 3 1 2
2 4 2 3
3 5 3 4
3 6 1 3
You can do
In [465]: pd.DataFrame((pd.factorize(df.values.ravel())[0]+1).reshape(df.shape),
columns=df.columns)
Out[465]:
P1 P2
0 1 2
1 2 3
2 3 4
3 1 3
Related
I have an Input file:
ID,ROLL_NO,ADM_DATE,FEES
1,12345,01/12/2016,500
2,12345,02/12/2016,200
3,987654,01/12/2016,1000
4,12345,03/12/2016,0
5,12345,04/12/2016,0
6,12345,05/12/2016,100
7,12345,06/12/2016,0
8,12345,07/12/2016,0
9,12345,08/12/2016,0
10,987654,02/12/2016,150
11,987654,03/12/2016,300
I'm trying to find maximum count of consecutive days where FEES is 0 for a particular ROLL_NO. If FEES is not equal to zero for consecutive days, max count will be zero for that particular ROLL_NO.
Expected Output:
ID,ROLL_NO,MAX_CNT -- First occurrence of ID for a particular ROLL_NO should come as ID in output
1,12345,3
3,987654,0
This is what I've come up with so far,
import pandas as pd
df = pd.read_csv('I5.txt')
df['COUNT'] = df.groupby(['ROLLNO','ADM_DATE'])['ROLLNO'].transform(pd.Series.value_counts)
print df
But I don't believe this is the right way to approach this.
Could someone help out a python newbie out here?
You can use:
#consecutive groups
r = df['ROLL_NO'] * df['FEES'].eq(0)
a = r.ne(r.shift()).cumsum()
print (a)
ID
1 1
2 1
3 1
4 2
5 2
6 3
7 4
8 4
9 4
10 5
11 5
dtype: int32
#filter 0 FEES, count, get max per first level and last add missing roll no by reindex
mask = df['FEES'].eq(0)
df = (df[mask].groupby(['ROLL_NO',a[mask]])
.size()
.max(level=0)
.reindex(df['ROLL_NO'].unique(), fill_value=0)
.reset_index(name='MAX_CNT'))
print (df)
ROLL_NO MAX_CNT
0 12345 3
1 987654 0
Explanation:
First compare FEES column with 0, eq is same as == and multiple mask by column ROLL_NO:
mask = df['FEES'].eq(0)
r = df['ROLL_NO'] * mask
print (r)
0 0
1 0
2 0
3 12345
4 12345
5 0
6 12345
7 12345
8 12345
9 0
10 0
dtype: int64
Get consecutive groups by compare shifted Series r and cumsum:
a = r.ne(r.shift()).cumsum()
print (a)
0 1
1 1
2 1
3 2
4 2
5 3
6 4
7 4
8 4
9 5
10 5
dtype: int32
Filter only 0 in FEES and groupby with size, also filter a for same indexes:
print (df[mask].groupby(['ROLL_NO',a[mask]]).size())
ROLL_NO
12345 2 2
4 3
dtype: int64
Get max values per first level of MultiIndex:
print (df[mask].groupby(['ROLL_NO',a[mask]]).size().max(level=0))
ROLL_NO
12345 3
dtype: int64
Last add missing ROLL_NO without 0 by reindex:
print (df[mask].groupby(['ROLL_NO',a[mask]])
.size()
.max(level=0)
.reindex(df['ROLL_NO'].unique(), fill_value=0))
ROLL_NO
12345 3
987654 0
dtype: int64
and for columns from index use reset_index.
EDIT:
For first ID use drop_duplicates with insert and map:
r = df['ROLL_NO'] * df['FEES'].eq(0)
a = r.ne(r.shift()).cumsum()
s = df.drop_duplicates('ROLL_NO').set_index('ROLL_NO')['ID']
mask = df['FEES'].eq(0)
df1 = (df[mask].groupby(['ROLL_NO',a[mask]])
.size()
.max(level=0)
.reindex(df['ROLL_NO'].unique(), fill_value=0)
.reset_index(name='MAX_CNT'))
df1.insert(0, 'ID', df1['ROLL_NO'].map(s))
print (df1)
ID ROLL_NO MAX_CNT
0 1 12345 3
1 3 987654 0
I tried to create a data frame df using the below code :
import numpy as np
import pandas as pd
index = [0,1,2,3,4,5]
s = pd.Series([1,2,3,4,5,6],index= index)
t = pd.Series([2,4,6,8,10,12],index= index)
df = pd.DataFrame(s,columns = ["MUL1"])
df["MUL2"] =t
print df
MUL1 MUL2
0 1 2
1 2 4
2 3 6
3 4 8
4 5 10
5 6 12
While trying to create the same data frame using the below syntax, I am getting a wierd output.
df = pd.DataFrame([s,t],columns = ["MUL1","MUL2"])
print df
MUL1 MUL2
0 NaN NaN
1 NaN NaN
Please explain why the NaN is being displayed in the dataframe when both the Series are non empty and why only two rows are getting displayed and no the rest.
Also provide the correct way to create the data frame same as has been mentioned above by using the columns argument in the pandas DataFrame method.
One of the correct ways would be to stack the array data from the input list holding those series into columns -
In [161]: pd.DataFrame(np.c_[s,t],columns = ["MUL1","MUL2"])
Out[161]:
MUL1 MUL2
0 1 2
1 2 4
2 3 6
3 4 8
4 5 10
5 6 12
Behind the scenes, the stacking creates a 2D array, which is then converted to a dataframe. Here's what the stacked array looks like -
In [162]: np.c_[s,t]
Out[162]:
array([[ 1, 2],
[ 2, 4],
[ 3, 6],
[ 4, 8],
[ 5, 10],
[ 6, 12]])
If remove columns argument get:
df = pd.DataFrame([s,t])
print (df)
0 1 2 3 4 5
0 1 2 3 4 5 6
1 2 4 6 8 10 12
Then define columns - if columns not exist get NaNs column:
df = pd.DataFrame([s,t], columns=[0,'MUL2'])
print (df)
0 MUL2
0 1.0 NaN
1 2.0 NaN
Better is use dictionary:
df = pd.DataFrame({'MUL1':s,'MUL2':t})
print (df)
MUL1 MUL2
0 1 2
1 2 4
2 3 6
3 4 8
4 5 10
5 6 12
And if need change columns order add columns parameter:
df = pd.DataFrame({'MUL1':s,'MUL2':t}, columns=['MUL2','MUL1'])
print (df)
MUL2 MUL1
0 2 1
1 4 2
2 6 3
3 8 4
4 10 5
5 12 6
More information is in dataframe documentation.
Another solution by concat - DataFrame constructor is not necessary:
df = pd.concat([s,t], axis=1, keys=['MUL1','MUL2'])
print (df)
MUL1 MUL2
0 1 2
1 2 4
2 3 6
3 4 8
4 5 10
5 6 12
A pandas.DataFrame takes in the parameter data that can be of type ndarray, iterable, dict, or dataframe.
If you pass in a list it will assume each member is a row. Example:
a = [1,2,3]
b = [2,4,6]
df = pd.DataFrame([a, b], columns = ["Col1","Col2", "Col3"])
# output 1:
Col1 Col2 Col3
0 1 2 3
1 2 4 6
You are getting NaN because it expects index = [0,1] but you are giving [0,1,2,3,4,5]
To get the shape you want, first transpose the data:
data = np.array([a, b]).transpose()
How to create a pandas dataframe
import pandas as pd
a = [1,2,3]
b = [2,4,6]
df = pd.DataFrame(dict(Col1=a, Col2=b))
Output:
Col1 Col2
0 1 2
1 2 4
2 3 6
Image with the csv file with the two columns
You can use:
df.drop_duplicates('Salesperson_1')
Or maybe need:
df.groupby('Salesperson_1')['Salesperson_1_ID'].transform('first')
Sample:
df = pd.DataFrame({'Salesperson_1':['a','a','b'],
'Salesperson_1_ID':[4,5,6]})
print (df)
Salesperson_1 Salesperson_1_ID
0 a 4
1 a 5
2 b 6
df1 = df.drop_duplicates('Salesperson_1')
print (df1)
Salesperson_1 Salesperson_1_ID
0 a 4
2 b 6
df.Salesperson_1_ID = df.groupby('Salesperson_1')['Salesperson_1_ID'].transform('first')
print (df)
Salesperson_1 Salesperson_1_ID
0 a 4
1 a 4
2 b 6
Pandas.groupby.first()
if your DataFrame is called df, you could just do this:
df.groupby('Salesperson_1_ID').first()
I have a list consisting of several string as shown below list=['abc','cde','fgh'] I want to add all the values to a particular index of dataframe. I am trying it with the code
df1.ix[1,3]=[list]
df1.to_csv('test.csv', sep=',')
I want in dataframe at poistion 1,3 all values to be inserted as it is ['abc','cde','fgh']. I don't want to convert it to string or any other format. But it is giving me error. what I am doing wrong here
I think you can use:
df1.ix[1,3] = L
Also is not recommended use variable list, because code word in python.
Sample:
df1 = pd.DataFrame({'a':[1,2,3], 'b':[1,2,3], 'c':[1,2,3], 'd':[1,2,3]})
print (df1)
a b c d
0 1 1 1 1
1 2 2 2 2
2 3 3 3 3
L = ['abc','cde','fgh']
df1.ix[1,3]= L
print (df1)
a b c d
0 1 1 1 1
1 2 2 2 [abc, cde, fgh]
2 3 3 3 3
I think you meant to use 1:3 not 1,3
consider the pd.DataFrame df
df = pd.DataFrame(dict(A=list('xxxxxxx')))
use loc or ix
df.loc[1:3, 'A'] = ['abc', 'cde', 'fgh']
or
df.ix[1:3, 'A'] = ['abc', 'cde', 'fgh']
yields
I have an xlsx file with over 1000 columns of data. I would like to firstly parse every second column from the data file (which can contain numbers and letters) and then create a unique list from the parsed data.
I'm a complete noob & have tried a "for" and "do while" loop but neither have worked for me.
So far I have:
import pandas as pd
workbook = pd.read_excel('C:\Python27\Scripts\Data.xlsx')
worksheet = workbook.sheetname='Data'
for col in range(worksheet[0], worksheet[1300]):
print(col)
I think I need to append the data and maybe write to a text file then create a unique list from the text file - I can do the second part it's just getting it into the text file I'm having trouble with.
Thanks
You can iterate over your columns by slicing and using a step arg i.e. df.ix[:, ::2]
In [35]:
df = pd.DataFrame({'a':1, 'b':[1,2,3,4,5], 'c':[2,3,4,5,6], 'd':0,'e':np.random.randn(5)})
df
Out[35]:
a b c d e
0 1 1 2 0 -0.352310
1 1 2 3 0 1.189140
2 1 3 4 0 -1.470507
3 1 4 5 0 0.742709
4 1 5 6 0 -2.798007
here we step every 2nd column:
In [37]:
df.ix[:,::2]
Out[37]:
a c e
0 1 2 -0.352310
1 1 3 1.189140
2 1 4 -1.470507
3 1 5 0.742709
4 1 6 -2.798007
we can then just call np.unique on the entire df to get a single array of all the unique values:
In [36]:
np.unique(df.ix[:,::2])
Out[36]:
array([-2.79800676, -1.47050675, -0.35231005, 0.74270934, 1. ,
1.18914011, 2. , 3. , 4. , 5. , 6. ])