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Conditional loop for pandas dataframe rows


By : Tom G
Date : October 17 2020, 08:10 PM
I wish did fix the issue. No need condition, just use logic of everything but last three characters, so logic written:
code :
2018.0
   ^^^
   removed
111.0
  ^^^
  removed
df['Contract']=df['Contract'].str[:-3]
print(df['Contract'])
0     201
1     201
2     201
3     201
4     131
5     123
6     123
7     123
8     123
9      11
10     11
11     11
12     11
Name: Contract, dtype: object


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Constructing pandas dataframe with rows conditional on their not existing in another dataframe python


By : user2719687
Date : March 29 2020, 07:55 AM
seems to work fine I have a pandas dataframe , This is one way
code :
gcols = ['store', 'date']
tmp[tmp.set_index(gcols).index.isin(df.set_index(gcols).index) == False]

Conditional operations for rows in pandas dataframe


By : obaid
Date : March 29 2020, 07:55 AM
This might help you You can use loc:
code :
df.loc[df.prediction < 0, 'return'] = 0
print df
    returns  prediction  return
0 -1.005705    0.999999     NaN
1  0.005952    1.000000     NaN
2  0.000000   -0.999891       0
3  0.020000   -1.000000       0
4  0.000000    1.000000     NaN
5  0.005000    1.000000     NaN
6  0.000000   -0.999984       0
7 -0.005813   -0.999871       0
df.loc[df.prediction < 0, 'returns'] = 0
print df
    returns  prediction
0 -1.005705    0.999999
1  0.005952    1.000000
2  0.000000   -0.999891
3  0.000000   -1.000000
4  0.000000    1.000000
5  0.005000    1.000000
6  0.000000   -0.999984
7  0.000000   -0.999871

Conditional based on slope between two rows in Pandas DataFrame


By : Ofer
Date : March 29 2020, 07:55 AM
should help you out I am trying to create a program that will select rows in a large time series dataframe and return only the rows where the slope (row2-row1)/(Time2-Time1) is >= the slope of the previous two rows (row1-row0)/(Time1-Time0). I have gone through some very heavy loop operations only to meet the extent for loops. , Let's start by calculating the dataframe of slopes:
code :
slopes = df.iloc[:, 1:].diff().div(df.Time.diff(), axis=0)
three_period_slopes = df.iloc[:, 1:].diff(2).div(df.Time.diff(2), axis=0)
gt_prior_slope = slopes >= slopes.shift()
gt_prior_2_slope_avg = slopes >= slopes.rolling(2).mean().shift()
gt_prior_3_period_slope = slopes >= three_period_slopes.shift()
df.C[gt_prior_slope.C]

2     6.860664
4     7.362645
7     8.894602
10    9.174402
Name: C, dtype: float64

Conditional mean and sum of previous N rows in pandas dataframe


By : Nath
Date : March 29 2020, 07:55 AM
I hope this helps you . Concerned is this exemplary pandas dataframe: , Heres an option, take the 3 period rolling mean and sum
code :
df['RollM'] = df.Measurement.rolling(window=3,min_periods=0).mean()

df['RollS'] = df.Measurement.rolling(window=3,min_periods=0).sum()
df.loc[df.Trigger == False,['RollS','RollM']] = np.nan
   Measurement  Trigger  Valid     RollM  RollS
0          2.0    False   True       NaN    NaN
1          4.0    False   True       NaN    NaN
2          3.0    False   True       NaN    NaN
3          0.0     True  False  2.333333    7.0
4        100.0    False   True       NaN    NaN
5          3.0    False   True       NaN    NaN
6          2.0    False   True       NaN    NaN
7          1.0     True   True  2.000000    6.0
df['mean'],df['sum'] = np.nan,np.nan

roller = df.Measurement.rolling(window=3,min_periods=0).agg(['mean','sum'])

df.loc[(df.Trigger == True) & (df.Valid == True),['mean','sum']] = roller

df.loc[(df.Trigger == True) & (df.Valid == False),['mean','sum']] = roller.shift(1)
  Measurement  Trigger  Valid  mean  sum
0          2.0    False   True   NaN  NaN
1          4.0    False   True   NaN  NaN
2          3.0    False   True   NaN  NaN
3          0.0     True  False   3.0  9.0
4        100.0    False   True   NaN  NaN
5          3.0    False   True   NaN  NaN
6          2.0    False   True   NaN  NaN
7          1.0     True   True   2.0  6.0

Conditional selecting rows in pandas DataFrame with MultiIndex


By : Somya Gupta
Date : March 29 2020, 07:55 AM
may help you . I have a DataFrame like this: , You are looking for any
code :
df[(df.loc[:,idx[:,['c']]]>0).any(axis = 1)]
Out[133]: 
          A                             B                    
          a         b         c         a         b         c
1 -0.423313  0.459464 -1.457655 -0.559667 -0.056230  1.338850
3 -0.072396  1.305868 -1.239441 -0.708834  0.348704  0.260532
4 -1.415575  1.229508  0.148254 -0.812806  1.379552 -1.195062
5 -0.336973 -0.469335  1.345719  0.847943  1.465100 -0.285792
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