For coercing pandas date times (stored as numpy datetime):
for col in df.columns[df.dtypes == np.dtype('<M8[ns]')]:
# https://stackoverflow.com/questions/32827169/python-reduce-precision-pandas-timestamp-dataframe
# apply(lambda x: x.replace(microsecond=0))
df[col] = df[col].values.astype('datetime64[s]')
For coercing python datetime (here, a datetime.date, there may be other options with datetime.datetime (I’ve included my failed attempts that may work there as comments)):
# df.date.values.to_timestamp()
# pd.Timestamp(df.date)
# df.date.values.astype(np.int64)
# df.loc[~df.date.isnull(),'date'].values.astype(np.int64)
# df.date.values[0].timestamp()
# pd.Timestamp(df.date.values[0],unit='s')
# pd.Timestamp(df.date.values[0].timestamp(),unit='s')
# df.date.values[0].isoformat()
# df.date.apply(lambda x: x.isoformat())
df['date'] = df.loc[~df.date.isnull(),'date'].apply(lambda x: x.isoformat())
For timedeltas in pandas, timedelta64[ns]:
df["timedelta_days"] = df.timedelta.dt.days
For mixed float and string, encoded as pandas object type and np.nan for nulls (this throws error):
df.loc[~df.col.apply(isfloat),"col"] = np.nan
You need these two, inefficient helpers:
def isint(x):
try:
int(x)
return True
except:
return False
def isfloat(x):
try:
float(x)
return True
except:
return False
When things come in with bytes type, and you get a memoryview error, hit your dataframe with this:
def stringify_df(df: pd.DataFrame):
for col in df.columns:
if df[col].dtype == "O":
if type(df[col].values[0]) == memoryview:
df.loc[~df[col].isnull(), [col]] = df.loc[~df[col].isnull(),:].apply(lambda x: x[col].tobytes().decode("ascii", "ignore"), axis=1)
else:
df.loc[~df[col].isnull(), [col]] = df.loc[~df[col].isnull(),:].apply(lambda x: x[col].encode("ascii", "ignore").decode("ascii", "ignore"), axis=1)
return df