I have a Pandas series of 10000 rows which is populated with a single alphabet, starting from A to Z.
However, I want to create dummy data frames for only A, B, and C, using Pandas get_dummies.
How do I go around doing that?
I don't want to get dummies for all the row values in the column and then select the specific columns, as the column contains other redundant data which eventually causes a Memory Error.
try this:
# create mock dataframe
df = pd.DataFrame( {'alpha':['a','a','b','b','c','e','f','g']})
# use replace with a regex to set characters d-z to None
pd.get_dummies(df.replace({'[^a-c]':None},regex =True))
output:
alpha_a alpha_b alpha_c
0 1 0 0
1 1 0 0
2 0 1 0
3 0 1 0
4 0 0 1
5 0 0 0
6 0 0 0
7 0 0 0
Related
I have a dataframe as follows:
import pandas as pd
df = pd.DataFrame({'sent.1':[0,1,0,1],
'sent.2':[0,1,1,0],
'sent.3':[0,0,0,1],
'sent.4':[1,1,0,1]
})
I am trying to replace the non-zero values with the 5th character in the column names (which is the numeric part of the column names), so the output should be,
sent.1 sent.2 sent.3 sent.4
0 0 0 0 4
1 1 2 0 4
2 0 2 0 0
3 1 0 3 4
I have tried the following but it does not work,
print(df.replace(1, pd.Series([i[5] for i in df.columns], [i[5] for i in df.columns])))
However when I replace it with column name, the above code works, so I am not sure which part is wrong.
print(df.replace(1, pd.Series(df.columns, df.columns)))
Since you're dealing with 1's and 0's, you can actually just use multiply the dataframe by a range:
df = df * range(1, df.shape[1] + 1)
Output:
sent.1 sent.2 sent.3 sent.4
0 0 0 0 4
1 1 2 0 4
2 0 2 0 0
3 1 0 3 4
Or, if you want to take the numbers from the column names:
df = df * df.columns.str.split('.').str[-1].astype(int)
you could use string multiplication on a boolean array to place the strings based on the condition, and where to restore the zeros:
mask = df.ne(0)
(mask*df.columns.str[5]).where(mask, 0)
To have integers:
mask = df.ne(0)
(mask*df.columns.str[5].astype(int))
output:
sent.1 sent.2 sent.3 sent.4
0 0 0 0 4
1 1 2 0 4
2 0 2 0 0
3 1 0 3 4
And another one, working with an arbitrary condition (here s.ne(0)):
df.apply(lambda s: s.mask(s.ne(0), s.name.rpartition('.')[-1]))
I have a Panda's dataframe like so:
colLabelA colLabelB ... colLabelZ
rowLabelA 10 0 0
specialRow 0 10 0
rowLabelB 20 0 10
...
rowLabelZ 0 0 20
Essentially I only know the row called specialRow. What I need is to find a way to iterate through the entire dataframe and check all the columns for 0 (zero).
If a column has all zeroes except specialRow, then that column by row cell needs to be made into a zero as well. Otherwise move to the next column and check that one.
So in the above example, only colLabelB has all zeroes except the specialRow so that needs to be updated like so:
colLabelA colLabelB ... colLabelZ
rowLabelA 10 0 0
specialRow 0 0 0
rowLabelB 20 0 10
...
rowLabelZ 0 0 20
Is there a quick and fast way to do this?
The dataframes aren't huge but I don't want it to be super slow either.
Use drop to drop the named row, then check for 0 with eq(0).all(). Then you can update with loc:
df.loc['specialRow', df.drop('specialRow').eq(0).all()] = 0
This works with more than one special rows too:
specialRows = ['specialRow']
df.loc[specialRows, df.drop(specialRows).eq(0).all()] = 0
Output:
colLabelA colLabelB colLabelZ
rowLabelA 10 0 0
specialRow 0 0 0
rowLabelB 20 0 10
rowLabelZ 0 0 20
For each column, exclude the particular index, then check if all other values for that column is zero, if yes, then just assign 0 to such columns:
for col in df:
if df[df.index!='specialRow'][col].eq(0).all():
df[col] = 0
OUTPUT:
colLabelA colLabelB colLabelZ
rowLabelA 10 0 0
specialRow 0 0 0
rowLabelB 20 0 10
rowLabelZ 0 0 20
In fact df.index!='specialRow' remains the same for all the columns, so you can just assign it to a variable and use it for each of the columns.
Drop the row with 'specialRow', check if column's values are all zero.
if (df.drop(['specialRow'])['colLabelB'] == 0).all():
df['B'] = 0
I have a data frame in R which, when running data$data['rs146217251',1:10,] looks something like:
g=0 g=1 g=2
1389117_1389117 0 1 NA
2912943_2912943 0 0 1
3094358_3094358 0 0 1
5502557_5502557 0 0 1
2758547_2758547 0 0 1
3527892_3527892 0 1 NA
3490518_3490518 0 0 1
1569224_1569224 0 0 1
4247075_4247075 0 1 NA
4428814_4428814 0 0 1
The leftmost column are participant identifiers. There are roughly 500,000 participants listed in this data frame, but I have a subset of them (about 5,000) listed by their identifiers. What is the best way to go about extracting only these rows that I care about according to their identifier in R or python (or some other way)?
Assuming that the participant identifiers are row names, you can filter by the vector of the identifiers as below:
df <- data$data['rs146217251',1:10,]
#Assuming the vector of identifiers
id <- c("4428814_4428814", "3490518_3490518", "3094358_3094358")
filtered <- df[id,]
Output:
> filtered
g.0 g.1 g.2
4428814_4428814 0 0 1
3490518_3490518 0 0 1
3094358_3094358 0 0 1
In my dataframe, I have a categorical variable that I'd like to convert into dummy variables. This column however has multiple values separated by commas:
0 'a'
1 'a,b,c'
2 'a,b,d'
3 'd'
4 'c,d'
Ultimately, I'd want to have binary columns for each possible discrete value; in other words, final column count equals number of unique values in the original column. I imagine I'd have to use split() to get each separate value but not sure what to do afterwards. Any hint much appreciated!
Edit: Additional twist. Column has null values. And in response to comment, the following is the desired output. Thanks!
a b c d
0 1 0 0 0
1 1 1 1 0
2 1 1 0 1
3 0 0 0 1
4 0 0 1 1
Use str.get_dummies
df['col'].str.get_dummies(sep=',')
a b c d
0 1 0 0 0
1 1 1 1 0
2 1 1 0 1
3 0 0 0 1
4 0 0 1 1
Edit: Updating the answer to address some questions.
Qn 1: Why is it that the series method get_dummies does not accept the argument prefix=... while pandas.get_dummies() does accept it
Series.str.get_dummies is a series level method (as the name suggests!). We are one hot encoding values in one Series (or a DataFrame column) and hence there is no need to use prefix. Pandas.get_dummies on the other hand can one hot encode multiple columns. In which case, the prefix parameter works as an identifier of the original column.
If you want to apply prefix to str.get_dummies, you can always use DataFrame.add_prefix
df['col'].str.get_dummies(sep=',').add_prefix('col_')
Qn 2: If you have more than one column to begin with, how do you merge the dummies back into the original frame?
You can use DataFrame.concat to merge one hot encoded columns with the rest of the columns in dataframe.
df = pd.DataFrame({'other':['x','y','x','x','q'],'col':['a','a,b,c','a,b,d','d','c,d']})
df = pd.concat([df, df['col'].str.get_dummies(sep=',')], axis = 1).drop('col', 1)
other a b c d
0 x 1 0 0 0
1 y 1 1 1 0
2 x 1 1 0 1
3 x 0 0 0 1
4 q 0 0 1 1
The str.get_dummies function does not accept prefix parameter, but you can rename the column names of the returned dummy DataFrame:
data['col'].str.get_dummies(sep=',').rename(lambda x: 'col_' + x, axis='columns')
Simple and practical question, yet I can't find a solution.
The questions I took a look were the following:
Modifying a subset of rows in a pandas dataframe
Changing certain values in multiple columns of a pandas DataFrame at once
Fastest way to copy columns from one DataFrame to another using pandas?
Selecting with complex criteria from pandas.DataFrame
The key difference between those and mine is that I need not to insert a single value, but a row.
My problem is, I pick up a row of a dataframe, say df1. Thus I have a series.
Now I have this other dataframe, df2, that I have selected multiple rows according to a criteria, and I want to replicate that series to all those row.
df1:
Index/Col A B C
1 0 0 0
2 0 0 0
3 1 2 3
4 0 0 0
df2:
Index/Col A B C
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
What I want to accomplish is inserting df1[3] into the lines df2[2] and df3[3] for example. So something like the non working code:
series = df1[3]
df2[df2.index>=2 and df2.index<=3] = series
returning
df2:
Index/Col A B C
1 0 0 0
2 1 2 3
3 1 2 3
4 0 0 0
Use loc and pass a list of the index labels of interest, after the following comma the : indicates we want to set all column values, we then assign the series but call attribute .values so that it's a numpy array. Otherwise you will get a ValueError as there will be a shape mismatch as you're intending to overwrite 2 rows with a single row and if it's a Series then it won't align as you desire:
In [76]:
df2.loc[[2,3],:] = df1.loc[3].values
df2
Out[76]:
A B C
1 0 0 0
2 1 2 3
3 1 2 3
4 0 0 0
Suppose you have to copy certain rows and columns from dataframe to some another data frame do this.
code
df2 = df.loc[x:y,a:b] // x and y are rows bound and a and b are column
bounds that you have to select