Pandas Appending a row to a dataframe and specify its index label

Question

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

User · Answer

df loc will do the job     gt  gt  gt  df   pd DataFrame np random randn 3  2   columns   A   B     gt  gt  gt  df           A         B 0 -0 269036  0 534991 1  0 069915 -1 173594 2 -1 177792  0 018381  gt  gt  gt  df loc 13    df loc 1   gt  gt  gt  df            A         B 0  -0 269036  0 534991 1   0 069915 -1 173594 2  -1 177792  0 018381 13  0 069915 -1 173594

User · Answer

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

User · Answer

I shall refer to the same sample of data as posted in the question    import numpy as np import pandas as pd df   pd DataFrame np random randn 8  4   columns   A   B   C   D    print  The original data frame is   n    format df     Running this code will give you  The original data frame is             A         B         C         D 0  0 494824 -0 328480  0 818117  0 100290 1  0 239037  0 954912 -0 186825 -0 651935 2 -1 818285 -0 158856  0 359811 -0 345560 3 -0 070814 -0 394711  0 081697 -1 178845 4 -1 638063  1 498027 -0 609325  0 882594 5 -0 510217  0 500475  1 039466  0 187076 6  1 116529  0 912380  0 869323  0 119459 7 -1 046507  0 507299 -0 373432 -1 024795   Now you wish to append a new row to this data frame  which doesn t need to be copy of any other row in the data frame   Alon suggested an interesting approach to use df loc to append a new row with different index  The issue  however  with this approach is if there is already a row present at that index  it will be overwritten by new values  This is typically the case for datasets when row index is not unique  like store ID in transaction datasets  So a more general solution to your question is to create the row  transform the new row data into a pandas series  name it to the index you want to have and then append it to the data frame  Don t forget to overwrite the original data frame with the one with appended row  The reason is df append returns a view of the dataframe and does not modify its contents  Following is the code   row   pd Series   A  10  B  20  C  30  D  40  name 3  df   df append row  print  The new data frame is   n    format df     Following would be the new output   The new data frame is              A          B          C          D 0   0 494824  -0 328480   0 818117   0 100290 1   0 239037   0 954912  -0 186825  -0 651935 2  -1 818285  -0 158856   0 359811  -0 345560 3  -0 070814  -0 394711   0 081697  -1 178845 4  -1 638063   1 498027  -0 609325   0 882594 5  -0 510217   0 500475   1 039466   0 187076 6   1 116529   0 912380   0 869323   0 119459 7  -1 046507   0 507299  -0 373432  -1 024795 3  10 000000  20 000000  30 000000  40 000000

User · Answer

There is another solution  The next code is bad  although I think pandas needs this feature   import pandas as pd    empty dataframe a   pd DataFrame   a loc 0      first   111   second   222   But the next code runs fine  import pandas as pd    empty dataframe a   pd DataFrame   a   a append pd Series   first   111   second   222   name 0

[python] Pandas: Appending a row to a dataframe and specify its index label

Examples related to python

Examples related to pandas