How to change the index order of a row in pandas
I have a dataframe like this:
100MHz_Dif0 102MHz_Dif0 100MHz_Dif1 102MHz_Dif1
Frequency
9.000000e+07 -70.209000 -65.174004 -66.063004 -66.490997
9.003333e+07 -70.628998 -65.196999 -66.339996 -66.461998
9.006667e+07 -70.405998 -65.761002 -65.432999 -65.549004
9.010000e+07 -70.524002 -65.552002 -66.038002 -65.887001
9.013333e+07 -70.746002 -65.658997 -65.086998 -65.390999
9.016667e+07 -70.884003 -66.209999 -64.887001 -65.397003
9.020000e+07 -70.752998 -66.019997 -65.308998 -66.571999
9.023333e+07 -70.447998 -65.858002 -65.500000 -65.028999
9.026667e+07 -70.452003 -65.832001 -66.032997 -65.005997
9.030000e+07 -71.219002 -65.739998 -65.961998 -65.986000
9.033333e+07 -71.095001 -65.820999 -67.112999 -65.977997
9.036667e+07 -70.834000 -65.926003 -66.348000 -65.568001
as an example. If I want to move the third row and fourth row to the first and second rows, which command should I use? And I will change the order of the lines depending on the frequency, and what can I do to implement it? Thank you very much.
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3 answers
For the order in which the index values change to, frequency
use sort_index
:
df = df.sort_index()
print (df)
100MHz_Dif0 102MHz_Dif0 100MHz_Dif1 102MHz_Dif1
Frequency
90000000.0 -70.209000 -65.174004 -66.063004 -66.490997
90033330.0 -70.628998 -65.196999 -66.339996 -66.461998
90066670.0 -70.405998 -65.761002 -65.432999 -65.549004
90100000.0 -70.524002 -65.552002 -66.038002 -65.887001
90133330.0 -70.746002 -65.658997 -65.086998 -65.390999
90166670.0 -70.884003 -66.209999 -64.887001 -65.397003
90200000.0 -70.752998 -66.019997 -65.308998 -66.571999
90233330.0 -70.447998 -65.858002 -65.500000 -65.028999
90266670.0 -70.452003 -65.832001 -66.032997 -65.005997
90300000.0 -71.219002 -65.739998 -65.961998 -65.986000
90333330.0 -71.095001 -65.820999 -67.112999 -65.977997
90366670.0 -70.834000 -65.926003 -66.348000 -65.568001
And for the sort columns:
df = df.sort_index(axis=1)
print (df)
100MHz_Dif0 100MHz_Dif1 102MHz_Dif0 102MHz_Dif1
Frequency
90000000.0 -70.209000 -66.063004 -65.174004 -66.490997
90033330.0 -70.628998 -66.339996 -65.196999 -66.461998
90066670.0 -70.405998 -65.432999 -65.761002 -65.549004
90100000.0 -70.524002 -66.038002 -65.552002 -65.887001
90133330.0 -70.746002 -65.086998 -65.658997 -65.390999
90166670.0 -70.884003 -64.887001 -66.209999 -65.397003
90200000.0 -70.752998 -65.308998 -66.019997 -66.571999
90233330.0 -70.447998 -65.500000 -65.858002 -65.028999
90266670.0 -70.452003 -66.032997 -65.832001 -65.005997
90300000.0 -71.219002 -65.961998 -65.739998 -65.986000
90333330.0 -71.095001 -67.112999 -65.820999 -65.977997
90366670.0 -70.834000 -66.348000 -65.926003 -65.568001
And for sorting - index
and columns
:
df = df.sort_index(axis=1).sort_index()
print (df)
100MHz_Dif0 100MHz_Dif1 102MHz_Dif0 102MHz_Dif1
Frequency
90000000.0 -70.209000 -66.063004 -65.174004 -66.490997
90033330.0 -70.628998 -66.339996 -65.196999 -66.461998
90066670.0 -70.405998 -65.432999 -65.761002 -65.549004
90100000.0 -70.524002 -66.038002 -65.552002 -65.887001
90133330.0 -70.746002 -65.086998 -65.658997 -65.390999
90166670.0 -70.884003 -64.887001 -66.209999 -65.397003
90200000.0 -70.752998 -65.308998 -66.019997 -66.571999
90233330.0 -70.447998 -65.500000 -65.858002 -65.028999
90266670.0 -70.452003 -66.032997 -65.832001 -65.005997
90300000.0 -71.219002 -65.961998 -65.739998 -65.986000
90333330.0 -71.095001 -67.112999 -65.820999 -65.977997
90366670.0 -70.834000 -66.348000 -65.926003 -65.568001
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Assuming the dataframe is named df
Use to create a matching snippet np.r_
df.iloc[np.r_[[2, 3], [0, 1], 4:]]
100MHz_Dif0 102MHz_Dif0 100MHz_Dif1 102MHz_Dif1
Frequency
90066670.0 -70.405998 -65.761002 -65.432999 -65.549004
90100000.0 -70.524002 -65.552002 -66.038002 -65.887001
90000000.0 -70.209000 -65.174004 -66.063004 -66.490997
90033330.0 -70.628998 -65.196999 -66.339996 -66.461998
90000000.0 -70.209000 -65.174004 -66.063004 -66.490997
90033330.0 -70.628998 -65.196999 -66.339996 -66.461998
90066670.0 -70.405998 -65.761002 -65.432999 -65.549004
90100000.0 -70.524002 -65.552002 -66.038002 -65.887001
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