I have two DataFrames: df1 and df2
both df1 and df2 are derived from the same original data set, which has a DatetimeIndex.
df2 still has a DatetimeIndex.
Whereas, df1 has been oversampled and now has an int index with the prior DatetimeIndex as a 'Date' column within it.
I need to reconstruct a df2 so that it aligns with df1, i.e. I'll need to oversample the rows that are oversampled and then order them and set them onto the same int index that df1 has.
Currently, I'm using these two functions below, but they are painfully slow. Is there any way to speed this up? I haven't been able to find any built-in function that does this. Is there?
def align_data(idx_col,data):
new_data = pd.DataFrame(index=idx_col.index,columns=data.columns)
for label,group in idx_col.groupby(idx_col):
if len(group.index) > 1:
slice = expanded(data.loc[label],len(group.index)).values
else:
slice = data.loc[label]
new_data.loc[group.index] = slice
return new_data
def expanded(row,l):
return pd.DataFrame(data=[row for i in np.arange(l)],index=np.arange(l),columns=row.index)
A test can be generated using the code below:
import pandas as pd
import numpy as np
import datetime as dt
dt_idx = pd.DatetimeIndex(start='1990-01-01',end='2018-07-02',freq='B')
df1 = pd.DataFrame(data=np.zeros((len(dt_idx),20)),index=dt_idx)
df1.index.name = 'Date'
df2 = df1.copy()
df1 = pd.concat([df1,df1.sample(len(dt_idx)/2)],axis=0)
df1.reset_index(drop=False,inplace=True)
t = dt.datetime.now()
df2_aligned = align_data(df1['Date'],df2)
print(dt.datetime.now()-t)
Related
I have two datasets. Below you can see codes and data
import pandas as pd
import numpy as np
pd.set_option('max_columns', None)
import matplotlib.pyplot as plt
data = {'type_sale': ['group_1','group_2','group_3','group_4','group_5','group_6','group_7','group_8','group_9','group_10'],
'id':[70,20,24,80,20,20,60,20,20,20],
}
df1 = pd.DataFrame(data, columns = ['type_sale',
'id',])
data = {'type_sale': ['group_1','group_2','group_3'],
'id':[70,20,24],
}
df2 = pd.DataFrame(data, columns = ['type_sale',
'id',])
These codes created two datasets that are shown below :
Now I want to create a new data set df3 with values from df1 that are different (distinct values) from the values df2 in the column id.
The final results should as pic below
I tried with these codes but are not giving desired results.
df = pd.concat((df1, df2))
print(df.drop_duplicates('id'))
So can anybody help me how to solve this problem?
Try as follows:
Use df.isin to check for each value in df['id'] whether it is contained in df2['id'].
Next, invert the resulting boolean pd.Series by using the unary operator ~ (tilde) and select from d1.
Finally, reset the index.
In a one-liner:
df3 = df1[~df1['id'].isin(df2['id'])].reset_index(drop=True)
print(df3)
type_sale id
0 group_4 80
1 group_7 60
When I shift my time series data, I get some NaNs in the dataframe. The only interpolation method that can replace these NaNs with numbers is 'linear'. The NaN are replaced by the same number, which isn't preferable.
Is there some way to instead use a different method like 'cubic' or 'quadratic'?
import numpy as np
import pandas as pd
# original data
df = pd.DataFrame()
np.random.seed(0)
days = pd.date_range(start='2015-01-01', end='2015-01-10', freq='1D')
df = pd.DataFrame({'Date': days, 'col1': np.random.randn(len(days))})
df = df.set_index('Date')
# add lags
df['lag1'] = df['col1'].shift(1)
df['lag3'] = df['col1'].shift(3)
print(df)
def interp(dfObj):
if dfObj.isna().sum()>0:
dfObj0 = dfObj.interpolate(method='linear', limit_direction='both')
return dfObj0
else:
return dfObj
df['lag1'] = interp(df['lag1'])
df['lag3'] = interp(df['lag3'])
print(df)
I believe this is due to the fact that the null values are at the beginning of the data frame, and as such the interpolation has no values to interpolate between. In this case, you need to perform extrapolation, for which pandas does not have a built in method. See this thread for more details.
Extrapolate Dataframe
I have a dataframe with several columns with dates - formatted as datetime.
I am trying to get the min/max value of a date, based on another date column being NaN
For now, I am doing this in two separate steps:
temp_df = df[(df['date1'] == np.nan)]
max_date = max(temp_df['date2'])
temp_df = None
I get the result I want, but I am using an unnecesary temporary dataframe.
How can I do this without it?
Is there any reference material to read on this?
Thanks
Here is an MCVE that can be played with to obtain statistics from other columns where the value in one isnull() (NaN or NaT). This can be done in a one-liner.
import pandas as pd
import numpy as np
print(pd.__version__)
# sample date columns
daterange1 = pd.date_range('2017-01-01', '2018-01-01', freq='MS')
daterange2 = pd.date_range('2017-04-01', '2017-07-01', freq='MS')
daterange3 = pd.date_range('2017-06-01', '2018-02-01', freq='MS')
df1 = pd.DataFrame(data={'date1': daterange1})
df2 = pd.DataFrame(data={'date2': daterange2})
df3 = pd.DataFrame(data={'date3': daterange3})
# jam them together, making NaT's in non-overlapping ranges
df = pd.concat([df1, df2, df3], axis=0, sort=False)
df.reset_index(inplace=True)
max_date = df[(df['date1'].isnull())]['date2'].max()
print(max_date)
I have a dataframe defined as follows:
import datetime
import pandas as pd
import random
import numpy as np
todays_date = datetime.datetime.today().date()
index = pd.date_range(todays_date - datetime.timedelta(10), periods=10, freq='D')
index = index.append(index)
idname = ['A']*10 + ['B']*10
values = random.sample(xrange(100), 20)
data = np.vstack((idname, values)).T
tmp_df = pd.DataFrame(data, columns=['id', 'value'])
tmp_index = pd.DataFrame(index, columns=['date'])
tmp_df = pd.concat([tmp_index, tmp_df], axis=1)
tmp_df = tmp_df.set_index('date')
Note that there are 2 values for each date. I would like to resample the dataframe tmp_df on a weekly basis but keep the two separate values. I tried tmp_df.resample('W-FRI') but it doesn't seem to work.
The solution you're looking for is groupby, which lets you perform operations on dataframe slices (here 'A' and 'B') independently:
df.groupby('id').resample('W-FRI')
Note: your code produces an error (No numeric types to aggregate) because the 'value' column is not converted to int. You need to convert it first:
df['value'] = pd.to_numeric(df['value'])
Consider the following code:
import datetime
import pandas as pd
import numpy as np
todays_date = datetime.datetime.now().date()
index = pd.date_range(todays_date-datetime.timedelta(10), periods=10, freq='D')
columns = ['A','B', 'C']
df_ = pd.DataFrame(index=index, columns=columns)
df_ = df_.fillna(0) # with 0s rather than NaNs
data = np.array([np.arange(10)]*3).T
df = pd.DataFrame(data, index=index, columns=columns)
df
Here we create an empty DataFrame in Python using Pandas and then fill it to any extent. However, is it possible to add columns dynamically in a similar manner, i.e., for columns = ['A','B', 'C'], it must be possible to add columns D,E,F etc till a specified number.
I think the
pandas.DataFrame.append
method is what you are after.
e.g.
output_frame=input_frame.append(appended_frame)
There are additional examples in the documentation Pandas merge join and concatenate documentation