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Is there any way to specify the index that I want for a new row, when appending the row to a dataframe?
The original documentation provides the following example:
In [1301]: df = DataFrame(np.random.randn(8, 4), columns=['A','B','C','D']) In [1302]: df Out[1302]: A B C D 0 -1.137707 -0.891060 -0.693921 1.613616 1 0.464000 0.227371 -0.496922 0.306389 2 -2.290613 -1.134623 -1.561819 -0.260838 3 0.281957 1.523962 -0.902937 0.068159 4 -0.057873 -0.368204 -1.144073 0.861209 5 0.800193 0.782098 -1.069094 -1.099248 6 0.255269 0.009750 0.661084 0.379319 7 -0.008434 1.952541 -1.056652 0.533946 In [1303]: s = df.xs(3) In [1304]: df.append(s, ignore_index=True) Out[1304]: A B C D 0 -1.137707 -0.891060 -0.693921 1.613616 1 0.464000 0.227371 -0.496922 0.306389 2 -2.290613 -1.134623 -1.561819 -0.260838 3 0.281957 1.523962 -0.902937 0.068159 4 -0.057873 -0.368204 -1.144073 0.861209 5 0.800193 0.782098 -1.069094 -1.099248 6 0.255269 0.009750 0.661084 0.379319 7 -0.008434 1.952541 -1.056652 0.533946 8 0.281957 1.523962 -0.902937 0.068159 where the new row gets the index label automatically. Is there any way to control the new label?
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Use concat() to Add a Row at Top of DataFrame concat([new_row,df. loc[:]]). reset_index(drop=True) to add the row to the first position of the DataFrame as Index starts from zero. reset_index() will reset the Index on the DataFrame to adjust the indexes on other rows.
Use concat() to Append Use pd. concat([new_row,df. loc[:]]). reset_index(drop=True) to append the row to the first position of the DataFrame as Index starts from zero.
append() function is used to append a single or a collection of indices together. In case of collection of indices, all of them gets appended to the original index in the same order as they are passed to the Index. append() function. The function returns an appended index.
The pandas. concat() method can also be used to add a column to the existing DataFrame by passing axis=1. This method will return the new DataFrame as the output, including the newly added column. Using the index, the above method will concatenate the Series with the original DataFrame.
The name of the Series becomes the index of the row in the DataFrame:
In [99]: df = pd.DataFrame(np.random.randn(8, 4), columns=['A','B','C','D']) In [100]: s = df.xs(3) In [101]: s.name = 10 In [102]: df.append(s) Out[102]: A B C D 0 -2.083321 -0.153749 0.174436 1.081056 1 -1.026692 1.495850 -0.025245 -0.171046 2 0.072272 1.218376 1.433281 0.747815 3 -0.940552 0.853073 -0.134842 -0.277135 4 0.478302 -0.599752 -0.080577 0.468618 5 2.609004 -1.679299 -1.593016 1.172298 6 -0.201605 0.406925 1.983177 0.012030 7 1.158530 -2.240124 0.851323 -0.240378 10 -0.940552 0.853073 -0.134842 -0.277135 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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