Delete row/ column in DataFrame with Null/ NaN values

if df0 is a Pandas DataFrame with null Values:

df0=df0.dropna(axis=0,how='all')  - will remove rows that have only 'NaN' values

df0=df0.dropna(axis=1,how='all')  - will remove columns that have only 'NaN' values

Find Maximum Values by year in timeseries dataframe, keeping the date

df2=df1.ix[df1.groupby(df1.index.year).idxmax().iloc[:,0]]

Find common timespan/ years in multiple time series/ dataframes

#concatenate vertically all dataframes
dfAlldf=pd.concat([df1, df2,df3,df4], axis=1)     

#sort them by the date (datetimeindex)

dfAlldf=dfAlldf.sort_index()    

#group dataframe by years

grps=dfAlldf.groupby(dfAlldf.index.year)           

#empty dataframe for populating with complete years

dfCompl=pd.DataFrame()

# for each group of years                                

for g in grps:

    #if don't have any null values in year, in any column 

    if not any(g[1].isnull().any(axis=1)):           

        #concatenate in dfCompl

        dfCompl=pd.concat([dfCompl, g[1]], axis=0)     

# re-sort by index

dfCompl=dfCompl.sort_index()                      

Pandas - Reading headers and dates correctly from Clipboard/ CSV

When using pandas funcions read_clipboard() or read_csv() you have to define if your data has headers (column headers) and indexes (row headers).

If you're passing indexes with datetime format, make sure if it will be parsed correctly, indicating it's a datetime and if it has dayfirst format (dd/mm/YYYY).

For example:

pd.read_clipboard(index_col=0, headers=None,parse_dates=True, dayfirst=True)

Is telling pandas that the table in clipboard has no column headers, but have index (row headers) in the first column and it is in datetime format with day first (dd/mm/YYYY).

Logarithmic and Exponential Curve Fit in Python - Numpy

With numpy function "polyfit":

X,y : data to be fitted

import numpy as np

1. Exponential fit

cf = np.polyfit(X, np.log(y), 1)

will return two coefficients, who will compose the equation:

exp(cf[1])*exp(cf[0]*X)


2. Logarithm fit:

cf = np.polyfit(np.log(X), y, 1)

will return two coefficients, who will compose the equation:

cf[0]*log(X)+cf[1]

Interpolate missing values in pandas DataFrame

If we have a dataframe with dates and flows - with missing values, as example below:

        0
2019-01-31 50.208308
2019-02-28 50.623457
2019-03-31 56.203933
2019-04-30 NaN
2019-05-31 NaN
2019-06-30 117.727655
2019-07-31 62.273259
2019-08-31 49.054898
2019-09-30 55.612575
2019-10-31 54.187409

We can use the function pandas interpolate, and interpolate the data with different methods

dfIn.interpolate() - will fill noData with linear interpolation;
dfIn.interpolate(method='polynomial', order=3) - will fill noData with 3rd degree polinomial interpolation;

Result:
                linear  polinomial    original
2019-01-31   50.208308   50.208308   50.208308
2019-02-28   50.623457   50.623457   50.623457
2019-03-31   56.203933   56.203933   56.203933
2019-04-30   76.711840   89.513986         NaN
2019-05-31   97.219748  124.233259         NaN
2019-06-30  117.727655  117.727655  117.727655
2019-07-31   62.273259   62.273259   62.273259
2019-08-31   49.054898   49.054898   49.054898
2019-09-30   55.612575   55.612575   55.612575
2019-10-31   54.187409   54.187409   54.187409