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Converting a generic float64 index to Timestamp in Pandas


Converting a generic float64 index to Timestamp in Pandas

By : user2950167
Date : November 17 2020, 11:52 AM
I think the issue was by ths following , Using today's date as the base, you can convert the floats to Timedelta values, interpreting the float values as seconds, and then adding that to today's date:
code :
In [15]: s2 = s.copy()

In [16]: s2.index = pd.Timestamp(datetime.date.today()) + pd.TimedeltaIndex(s.index, unit='s')

In [17]: s2
Out[17]: 
2014-12-30 00:27:11.083000   -5000
2014-12-30 00:27:12.395000   -5000
2014-12-30 00:27:15.482000   -5000
2014-12-30 00:27:18.536000   -1800
2014-12-30 00:27:20.818000   -5000
2014-12-30 00:27:24.739000   -5000
2014-12-30 00:27:24.828000   -5000
2014-12-30 00:27:35.214000   -1800
2014-12-30 00:27:38.691000   -4300
2014-12-30 00:27:42.751000   -5000
2014-12-30 00:28:13.350000   -5000
Name: quantity, dtype: int64


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pandas - change df.index from float64 to unicode or string

pandas - change df.index from float64 to unicode or string


By : Ольга Бабеева
Date : March 29 2020, 07:55 AM
Hope that helps I want to change a dataframes' index (rows) from float64 to string or unicode. , You can do it that way:
code :
# for Python 2
df.index = df.index.map(unicode) 

# for Python 3 (the unicode type does not exist and is replaced by str)
df.index = df.index.map(str)
Pandas: Cannot convert type <class 'pandas.tseries.index.DatetimeIndex'> to Timestamp

Pandas: Cannot convert type <class 'pandas.tseries.index.DatetimeIndex'> to Timestamp


By : Xin Tian Oon
Date : March 29 2020, 07:55 AM
hope this fix your issue I think yous need [0] for convert DatetimeIndex as array with one value to scalar:
code :
triggerDate = dat.loc[dat.Close <= threshold[0]][:1].index[0]
triggerDate = dat.index[dat.Close <= threshold[0]][0]
Pandas: converting timestamp into datetime

Pandas: converting timestamp into datetime


By : Alloice Lagat
Date : March 29 2020, 07:55 AM
To fix the issue you can do I have a column of date time strings in UTC which I need to convert to a viable datetime format in EST using pandas. I successfully converted the column, however I feel that my solution is long-winded and can be simplified, but I'm not sure how. Here is the way I'm currently converting the dates: , I will do tz_localize and tz_localize
code :
pd.to_datetime(df.date).dt.tz_localize('UTC').\
     dt.tz_convert('EST').\
      dt.strftime('%Y-%m-%d %H:%M:%S')
0    2014-02-06 19:25:40
1    2014-02-06 19:25:41
2    2014-02-06 19:25:42
3    2014-02-06 19:25:42
4    2014-02-06 19:25:43
5    2014-02-06 19:25:43
6    2014-02-06 19:25:44
7    2014-02-06 19:25:44
8    2014-02-06 19:25:44
Name: date, dtype: object
Pandas : ValueError ( any way to convert Sparse[float64, 0.0] dtypes to float64 datatype )

Pandas : ValueError ( any way to convert Sparse[float64, 0.0] dtypes to float64 datatype )


By : Silver
Date : March 29 2020, 07:55 AM
To fix the issue you can do I have a dataframe X_train to which i am concatenating a couple of another dataframe. This second & third dataframe is obtained from sparse matrix which has been been generated by a TF-IDF VEctorizer
code :
X_train_final = hstack( blocks=(x_tr_cwc_min,\
                            x_tr_cwc_max,\
                            x_tr_csc_min,\
                            x_tr_csc_max,\
                            x_tr_ctc_min,\
                            x_tr_ctc_max,\
                            x_tr_last_word_eq,\
                            x_tr_first_word_eq,\
                            x_tr_abs_len_diff,\
                            x_tr_mean_len,\
                            x_tr_token_set_ratio,\
                            x_tr_token_sort_ratio,\
                            x_tr_fuzz_ratio,\
                            x_tr_fuzz_partial_ratio,\
                            x_tr_longest_substr_ratio,\
                            q1_tdidf_train,q2_tdidf_train
                           )
                  ).tocsr()
sum of 'float64' column type in pandas return float instead of numpy.float64

sum of 'float64' column type in pandas return float instead of numpy.float64


By : Roshan Rijal
Date : March 29 2020, 07:55 AM
will help you This seems to be from the way that pandas is handling nans. When I set skipna=False in the sum method I get the numpy datatype
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