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Why my code didn't select data from Pandas dataframe?


Why my code didn't select data from Pandas dataframe?

By : hejar saadeh
Date : November 22 2020, 04:01 AM
wish of those help datetime.date objects are not vectorised with Pandas. The docs indicate this:
code :
data['time'] = pd.to_datetime(data['time'])
select_date = data.loc[data['time'].dt.normalize() == '2018-01-17']
for day, day_df in data.groupby(data['time'].dt.normalize()):
    # do something with day_df


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Including a row in the result of a SELECT for which the select didnt recover any data

Including a row in the result of a SELECT for which the select didnt recover any data


By : Denny Tsai
Date : March 29 2020, 07:55 AM
hope this fix your issue I'm trying write a query to retrieve the following data : , Try:
code :
SELECT TYPE              AS "ORDERS", 
       Count(DATE_ORDER) AS "TOTAL", 
       Nvl(Sum(CASE 
                 WHEN (DATE_ORDER BETWEEN SYSDATE - 10 AND SYSDATE) THEN 1 
                 ELSE 0 
               END), 0)  AS "0_10_DAYS", 
       Nvl(Sum(CASE 
                 WHEN (DATE_ORDER BETWEEN SYSDATE - 20 AND SYSDATE - 11) THEN 1
                 ELSE 0 
               END), 0)  AS "10_20_DAYS", 
       Nvl(Sum(CASE 
                 WHEN (DATE_ORDER BETWEEN SYSDATE - 30 AND SYSDATE - 21) THEN 1
                 ELSE 0 
               END), 0)  AS "20_30_DAYS", 
       Nvl(Sum(CASE 
                 WHEN (DATE_ORDER <= To_date(SYSDATE - 30)) THEN 1 
                 ELSE 0 
               END), 0)  AS "PLUS_30_DAYS" 
FROM   (SELECT TYPE, DATE_ORDER 
        FROM T_ORDERS 
        WHERE TYPE IN ('X', 'Y', 'Z')
        UNION ALL
        SELECT DECODE(LEVEL, 1,'X', 2,'Y', 3,'Z') TYPE, NULL DATE_ORDER 
        FROM DUAL
        CONNECT BY LEVEL <= 3 
       ) SQ
GROUP  BY TYPE 
pandas(dataframe) select data

pandas(dataframe) select data


By : Michel Kovalov
Date : March 29 2020, 07:55 AM
I hope this helps . Hello guys, , Looking at what you're doing it will be more performant to just do:
code :
C_table = pd.concat([C_table, B_table[B_table['ID'].isin(A_table['ID'])]])
B_table[B_table['ID'].isin(A_table['ID'])]
select rows with the same data from pandas dataframe

select rows with the same data from pandas dataframe


By : 0knwlmgofp
Date : March 29 2020, 07:55 AM
it should still fix some issue I think the best is groupby by dates and apply some function like mean, sum or apply with custom function:
code :
df1 = df.groupby(df['StartDate'].dt.date).mean()

df2 = df.groupby(df['StartDate'].dt.date).apply(func)
#some sample function
def func(df_day,parameters):
    #print each group
    print (df_day)

    return df_day['StartDate'] - pd.Timedelta(parameters, unit='d')

df2 = df.groupby(df['StartDate'].dt.date).apply(lambda x: func(x, 1))
#less readable
#df2 = df.groupby(df['StartDate'].dt.date).apply(func, 1)
print (df2)
StartDate    
2015-03-25  0   2015-03-24 12:25:43.999994
            1   2015-03-24 13:23:43.999998
            2   2015-03-24 13:24:43.999994
2015-03-26  3   2015-03-25 13:25:44.000001
            4   2015-03-25 13:47:43.999992
            5   2015-03-25 13:48:43.999999
2015-03-27  6   2015-03-26 14:25:43.999997
            7   2015-03-26 15:25:43.999994
            8   2015-03-26 15:28:43.999993
            9   2015-03-26 15:29:44.000000
Name: StartDate, dtype: datetime64[ns]
how to select partial data from a pandas dataframe

how to select partial data from a pandas dataframe


By : Farid Lavizadeh
Date : March 29 2020, 07:55 AM
To fix the issue you can do You can find last valid index based on your condition and create a slice.
code :
idx = df[df['cumsum(flag)'].abs() <= 1].last_valid_index()
df.loc[:idx, :]

    ticker  value   flag    cumsum(flag)
0   A1      -1.5    -1      -1
1   B1       1.4     1       0
2   C3      -2.4    -1      -1
Pandas: Select all data from Pandas DataFrame between two dates

Pandas: Select all data from Pandas DataFrame between two dates


By : Fabio.sassu
Date : March 29 2020, 07:55 AM
I hope this helps you . I have DataFrame in which there is a column with event dates (dates are not unique). I need to select all the data that is in this period. I try next: , I used the following test DataFrame:
code :
   Transaction_date  Value
0        2019-12-31     10
1        2020-01-10     10
2        2020-01-15     10
3        2020-01-20     10
4        2020-01-25     10
5        2020-01-28     10
6        2020-01-29     10
7        2020-01-30     10
8        2020-01-31     10
9        2020-02-01     10
10       2020-02-01     10
start_day = '01.01.2020'
end_day = '31.01.2020'
# Convert start / end dates to datetime
start_day = pd.to_datetime(start_day)
end_day = pd.to_datetime(end_day)
df[df['Transaction_date'].between(start_day, end_day)]
  Transaction_date  Value
1       2020-01-10     10
2       2020-01-15     10
3       2020-01-20     10
4       2020-01-25     10
5       2020-01-28     10
6       2020-01-29     10
7       2020-01-30     10
8       2020-01-31     10
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