We Keep Coding

Not-quite gradient of dataframe

I have a dataframe of ints:
mydf = pd.DataFrame([[0,0,0,1,0,2,2,5,2,4],
[0,1,0,0,2,2,4,5,3,3],
[1,1,1,1,2,2,0,4,4,4]])
I'd like to calculate something that resembles the gradient given by pd.Series.dff() for each row, but with one big change: my ints represent categorical data, so I'm only interested in detecting a change, not the magnitude of it. So the step from 0 to 1 should be the same as the step from 0 to 4.
Is there a way for pandas to interpret my data as categorical in the data frame, and then calculate a Series.diff() on that? Or could you "flatten" the output of Series.diff() to be only 0s and 1s?

If I understand you correctly, this is what you are trying to achieve:
import pandas as pd
mydf = pd.DataFrame([[0,0,0,1,0,2,2,5,2,4],
[0,1,0,0,2,2,4,5,3,3],
[1,1,1,1,2,2,0,4,4,4]])
mydf = mydf.astype("category")
diff_df = mydf.apply(lambda x: x.diff().ne(0), axis=1).astype(int)
The ne returns a boolean array which indicates if the difference between consecutive values is different from zero. Then you use the astype to convert the boolean values to integers (0s and 1s). The result is a dataframe with the same number of rows as the original dataframe, and the same number of columns, but with binary values indicating a change in the categorical value from one step to the next.
0 1 2 3 4 5 6 7 8 9
0 1 0 0 1 1 1 0 1 1 1
1 1 1 1 0 1 0 1 1 1 0
2 1 0 0 0 1 0 1 1 0 0

pandas: replace values in column with the last character in the column name

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]))

Copy pandas DataFrame row to multiple other rows

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

Transform pandas timeseries into timeseries with non-date index

I'm trying to generate a timeseries from a dataframe, but the solutions I've found here don't really address my specific problem. I have a dataframe which is a series of id's which iterate from 1 to n, then repeat, like this:
key ID Var_1
0 1 1
0 2 1
0 3 2
1 1 3
1 2 2
1 3 1
I want to reshape it into a timeseries in which the index
ID Var_1_0 Var_2_0
1 1 3
2 1 2
3 2 1
I have tried the stack() method but it doesn't generate the result I want. Generating an index from ID seems to be the right ID is not a proper date so I'm not sure how to proceed. Pointers much appreciated.

Try this:
import pandas as pd
df = pd.DataFrame([[0,1,1], [0,2,1], [0,3,2], [1,1,3], [1,2,2], [1,3,1]], columns=('key', 'ID', 'Var_1'))
Use the pivot function:
df2 = df.pivot('ID', 'key', 'Var_1')
You can rename the columns by:
df2.columns = ('Var_1_0', 'Var_2_0')
Result:
Out:
Var_1_0 Var_2_0
ID
1 1 3
2 1 2
3 2 1

Drop Rows by Multiple Column Criteria in DataFrame

I have a pandas dataframe that I'm trying to drop rows based on a criteria across select columns. If the values in these select columns are zero, the rows should be dropped. Here is an example.
import pandas as pd
t = pd.DataFrame({'a':[1,0,0,2],'b':[1,2,0,0],'c':[1,2,3,4]})
a b c
0 1 1 1
1 0 2 2
2 0 0 3
3 2 0 4
I would like to try something like:
cols_of_interest = ['a','b'] #Drop rows if zero in all these columns
t = t[t[cols_of_interest]!=0]
This doesn't drop the rows, so I tried:
t = t.drop(t[t[cols_of_interest]==0].index)
And all rows are dropped.
What I would like to end up with is:
a b c
0 1 1 1
1 0 2 2
3 2 0 4
Where the 3rd row (index 2) was dropped because it took on value 0 in BOTH the columns of interest, not just one.

Your problem here is that you first assigned the result of your boolean condition: t = t[t[cols_of_interest]!=0] which overwrites your original df and sets where the condition is not met with NaN values.
What you want to do is generate the boolean mask, then drop the NaN rows and pass thresh=1 so that there must be at least a single non-NaN value in that row, we can then use loc and use the index of this to get the desired df:
In [124]:
cols_of_interest = ['a','b']
t.loc[t[t[cols_of_interest]!=0].dropna(thresh=1).index]
Out[124]:
a b c
0 1 1 1
1 0 2 2
3 2 0 4
EDIT
As pointed out by #DSM you can achieve this simply by using any and passing axis=1 to test the condition and use this to index into your df:
In [125]:
t[(t[cols_of_interest] != 0).any(axis=1)]
Out[125]:
a b c
0 1 1 1
1 0 2 2
3 2 0 4