python - pandas shift converts my column from integer to float.

Question

shift converts my column from integer to float. It turns out that np.nan is float only. Is there any ways to keep shifted column as integer?

df = pd.DataFrame({"a":range(5)})
df['b'] = df['a'].shift(1)

df['a']
# 0    0
# 1    1
# 2    2
# 3    3
# 4    4
# Name: a, dtype: int64

df['b']

# 0   NaN
# 1     0
# 2     1
# 3     2
# 4     3
# Name: b, dtype: float64

Answer 1

Solution for pandas under 0.24:

Problem is you get NaN value what is float, so int is converted to float - see na type promotions.

One possible solution is convert NaN values to some value like 0 and then is possible convert to int:

df = pd.DataFrame({"a":range(5)})
df['b'] = df['a'].shift(1).fillna(0).astype(int)
print (df)
   a  b
0  0  0
1  1  0
2  2  1
3  3  2
4  4  3

Solution for pandas 0.24+ - check Series.shift:

fill_value object, optional
The scalar value to use for newly introduced missing values. the default depends on the dtype of self. For numeric data, np.nan is used. For datetime, timedelta, or period data, etc. NaT is used. For extension dtypes, self.dtype.na_value is used.

Changed in version 0.24.0.

df['b'] = df['a'].shift(fill_value=0)