Reputation: 153
I want to duplicate the rows of dataframe "this" according to 2 column values and save them as a new dataframe named "newThis":
this = pd.DataFrame(columns=['a','b','c'], index=[1,2,3])
this.a = [1, 2, 0]
this.b = [5, 0, 4]
this.c = [2, 3, 2]
newThis = []
for i in range(len(this)):
if int(this.iloc[i, 1]) != 0:
that = np.array([this.iloc[i,:]] * int(this.iloc[i, 1]))
elif int(this.iloc[i, 1]) == 0:
that = np.array([this.iloc[i,:]])
if int(this.iloc[i, 2]) != 0:
those = np.array([this.iloc[i,:]] * int(this.iloc[i, 2]))
elif int(this.iloc[i, 2]) == 0:
those = np.array([this.iloc[i,:]])
newThis.append(that)
newThis.append(those)
I want one big array of concatenated rows, but Instead I get this mess:
[array([[1, 5, 2],
[1, 5, 2],
[1, 5, 2],
[1, 5, 2],
[1, 5, 2]], dtype=int64), array([[1, 5, 2],
[1, 5, 2]], dtype=int64), array([[2, 0, 3]], dtype=int64), array([[2, 0, 3],
[2, 0, 3],
[2, 0, 3]], dtype=int64), array([[0, 4, 2],
[0, 4, 2],
[0, 4, 2],
[0, 4, 2]], dtype=int64), array([[0, 4, 2],
[0, 4, 2]], dtype=int64)]
Thanks
Upvotes: 2
Views: 1883
Reputation: 210862
IIUC:
Source DF:
In [213]: this
Out[213]:
a b c
1 1 5 2
2 2 0 3
3 0 4 2
Solution:
In [211]: newThis = pd.DataFrame(np.repeat(this.values,
this['b'].replace(0,1).tolist(),
axis=0),
columns=this.columns)
In [212]: newThis
Out[212]:
a b c
0 1 5 2
1 1 5 2
2 1 5 2
3 1 5 2
4 1 5 2
5 2 0 3
6 0 4 2
7 0 4 2
8 0 4 2
9 0 4 2
Upvotes: 3
Reputation: 82028
It looks like you're confusing multiplying an np.array with a list.
Remember:
[np.int32(1)] * 2 == [np.int32(1), np.int32(1)]
But:
np.array([1]) * 2 == np.array([2])
You probably need to change this:
np.array([this.iloc[i,:]] * int(this.iloc[i, 1]))
to this:
np.array([this.iloc[i,:]]) * int(this.iloc[i, 1])
Upvotes: 0