Code Sample, a copy-pastable example if possible
s = pd.Series(np.random.randint(0, 10, 1000, dtype=np.int64))
df = pd.crosstab(s.shift(1), s)
print(df.index.dtype)
print(df.columns.dtype)
Problem description
Outputs:
dtype('float64')
dtype('int64')
Maybe both the index and columns keep the original dtype of the series.
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Expected Output
dtype('int64')
dtype('int64')
Output of pd.show_versions()
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.3.final.0
python-bits: 64
OS: Darwin
OS-release: 16.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
pandas: 0.20.3
pytest: 3.2.1
pip: 9.0.1
setuptools: 36.5.0
Cython: 0.26
numpy: 1.13.3
scipy: 1.0.0
xarray: 0.9.6
IPython: 6.1.0
sphinx: 1.6.3
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.1
tables: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.8
xlrd: 1.0.0
xlwt: None
xlsxwriter: None
lxml: 3.8.0
bs4: 4.6.0
html5lib: 0.9999999
sqlalchemy: 1.1.13
pymysql: 0.7.11.None
psycopg2: 2.7.3 (dt dec pq3 ext lo64)
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: 0.5.0
Comment From: jreback
This is correct
introduces nans and so dtype changes to float
In [5]: s.shift(1).dtype
Out[5]: dtype('float64')
you should fill them and astype if you want.
In [4]: s = pd.Series(np.random.randint(0, 10, 1000, dtype=np.int64))
...: df = pd.crosstab(s.shift(1).fillna(0).astype(int), s)
...: print(df.index.dtype)
...: print(df.columns.dtype)
...:
int64
int64