Shifting multi-column data to the left Pandas Dataframe [duplicate]

This question already has an answer here:

Left justify string values in a pandas DataFrame

1 answer

I'd like to make whole columns data moved to left.
Only data should be shifted to the left 1 time and the columns should be fixed.
Could you tell me how can I do this?

Initial Data is :

              A1   A2   A3   A4   A5

     #0001    421  000  000  777  888

     #0002    382  403  430  320  055

     #0003    441  304  403  403  403

     #0004    430  403  206  N/A  312

     #0005    N/A  394  493  N/A  403

Desire Data is :

              A1   A2   A3   A4   A5

     #0001    000  000  777  888  N/A

     #0002    403  430  320  055  N/A

     #0003    304  403  403  403  N/A

     #0004    403  206  N/A  312  N/A

     #0005    394  493  N/A  403  N/A

asked Nov 15 '18 at 4:41

Landon Nam

212

marked as duplicate by coldspeed pandas
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Nov 15 '18 at 5:32

This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.

stackoverflow.com/a/47898659/4909087 works for numeric data, check the link.

– coldspeed
Nov 15 '18 at 5:32

@coldspeed Can you please write a comment on how to use justify to this scenario. To learn how it works before deleting my answer.

– Sandeep Kadapa
Nov 15 '18 at 5:59

@SandeepKadapa If they are numbers, you can simply pass an array to justify: v = df.values; v = justify(v); df.values[:] = v

– coldspeed
Nov 15 '18 at 6:02

@coldspeed Checked isn't giving right results also the output array contains extreme values -2147483648 in place of np.nan. Could you verify it?.

– Sandeep Kadapa
Nov 15 '18 at 6:10

@coldspeed : that question is quite different. This question can be solved by much simpler answers.

– rnso
Nov 15 '18 at 6:38

add a comment |

This question already has an answer here:

Left justify string values in a pandas DataFrame

1 answer

I'd like to make whole columns data moved to left.
Only data should be shifted to the left 1 time and the columns should be fixed.
Could you tell me how can I do this?

Initial Data is :

              A1   A2   A3   A4   A5

     #0001    421  000  000  777  888

     #0002    382  403  430  320  055

     #0003    441  304  403  403  403

     #0004    430  403  206  N/A  312

     #0005    N/A  394  493  N/A  403

Desire Data is :

              A1   A2   A3   A4   A5

     #0001    000  000  777  888  N/A

     #0002    403  430  320  055  N/A

     #0003    304  403  403  403  N/A

     #0004    403  206  N/A  312  N/A

     #0005    394  493  N/A  403  N/A

asked Nov 15 '18 at 4:41

Landon Nam

212

marked as duplicate by coldspeed pandas
Users with the pandas badge can single-handedly close pandas questions as duplicates and reopen them as needed.

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Nov 15 '18 at 5:32

This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.

stackoverflow.com/a/47898659/4909087 works for numeric data, check the link.

– coldspeed
Nov 15 '18 at 5:32

@coldspeed Can you please write a comment on how to use justify to this scenario. To learn how it works before deleting my answer.

– Sandeep Kadapa
Nov 15 '18 at 5:59

@SandeepKadapa If they are numbers, you can simply pass an array to justify: v = df.values; v = justify(v); df.values[:] = v

– coldspeed
Nov 15 '18 at 6:02

@coldspeed Checked isn't giving right results also the output array contains extreme values -2147483648 in place of np.nan. Could you verify it?.

– Sandeep Kadapa
Nov 15 '18 at 6:10

@coldspeed : that question is quite different. This question can be solved by much simpler answers.

– rnso
Nov 15 '18 at 6:38

add a comment |

This question already has an answer here:

Left justify string values in a pandas DataFrame

1 answer

I'd like to make whole columns data moved to left.
Only data should be shifted to the left 1 time and the columns should be fixed.
Could you tell me how can I do this?

Initial Data is :

              A1   A2   A3   A4   A5

     #0001    421  000  000  777  888

     #0002    382  403  430  320  055

     #0003    441  304  403  403  403

     #0004    430  403  206  N/A  312

     #0005    N/A  394  493  N/A  403

Desire Data is :

              A1   A2   A3   A4   A5

     #0001    000  000  777  888  N/A

     #0002    403  430  320  055  N/A

     #0003    304  403  403  403  N/A

     #0004    403  206  N/A  312  N/A

     #0005    394  493  N/A  403  N/A

asked Nov 15 '18 at 4:41

Landon Nam

212

This question already has an answer here:

Left justify string values in a pandas DataFrame

1 answer

I'd like to make whole columns data moved to left.
Only data should be shifted to the left 1 time and the columns should be fixed.
Could you tell me how can I do this?

Initial Data is :

              A1   A2   A3   A4   A5

     #0001    421  000  000  777  888

     #0002    382  403  430  320  055

     #0003    441  304  403  403  403

     #0004    430  403  206  N/A  312

     #0005    N/A  394  493  N/A  403

Desire Data is :

              A1   A2   A3   A4   A5

     #0001    000  000  777  888  N/A

     #0002    403  430  320  055  N/A

     #0003    304  403  403  403  N/A

     #0004    403  206  N/A  312  N/A

     #0005    394  493  N/A  403  N/A

This question already has an answer here:

Left justify string values in a pandas DataFrame

1 answer

python pandas

asked Nov 15 '18 at 4:41

Landon Nam

212

asked Nov 15 '18 at 4:41

Landon Nam

212

asked Nov 15 '18 at 4:41

Landon Nam

212

asked Nov 15 '18 at 4:41

Landon Nam

212

asked Nov 15 '18 at 4:41

Landon Nam

212

marked as duplicate by coldspeed pandas
Users with the pandas badge can single-handedly close pandas questions as duplicates and reopen them as needed.

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Nov 15 '18 at 5:32

This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.

marked as duplicate by coldspeed pandas
Users with the pandas badge can single-handedly close pandas questions as duplicates and reopen them as needed.

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Nov 15 '18 at 5:32

This question has been asked before and already has an answer. If those answers do not fully address your question, please ask a new question.

stackoverflow.com/a/47898659/4909087 works for numeric data, check the link.

– coldspeed
Nov 15 '18 at 5:32

@coldspeed Can you please write a comment on how to use justify to this scenario. To learn how it works before deleting my answer.

– Sandeep Kadapa
Nov 15 '18 at 5:59

@SandeepKadapa If they are numbers, you can simply pass an array to justify: v = df.values; v = justify(v); df.values[:] = v

– coldspeed
Nov 15 '18 at 6:02

@coldspeed Checked isn't giving right results also the output array contains extreme values -2147483648 in place of np.nan. Could you verify it?.

– Sandeep Kadapa
Nov 15 '18 at 6:10

@coldspeed : that question is quite different. This question can be solved by much simpler answers.

– rnso
Nov 15 '18 at 6:38

add a comment |

stackoverflow.com/a/47898659/4909087 works for numeric data, check the link.

– coldspeed
Nov 15 '18 at 5:32

@coldspeed Can you please write a comment on how to use justify to this scenario. To learn how it works before deleting my answer.

– Sandeep Kadapa
Nov 15 '18 at 5:59

@SandeepKadapa If they are numbers, you can simply pass an array to justify: v = df.values; v = justify(v); df.values[:] = v

– coldspeed
Nov 15 '18 at 6:02

@coldspeed Checked isn't giving right results also the output array contains extreme values -2147483648 in place of np.nan. Could you verify it?.

– Sandeep Kadapa
Nov 15 '18 at 6:10

@coldspeed : that question is quite different. This question can be solved by much simpler answers.

– rnso
Nov 15 '18 at 6:38

stackoverflow.com/a/47898659/4909087 works for numeric data, check the link.

– coldspeed
Nov 15 '18 at 5:32

@coldspeed Can you please write a comment on how to use justify to this scenario. To learn how it works before deleting my answer.

– Sandeep Kadapa
Nov 15 '18 at 5:59

@SandeepKadapa If they are numbers, you can simply pass an array to justify: v = df.values; v = justify(v); df.values[:] = v

– coldspeed
Nov 15 '18 at 6:02

@coldspeed Checked isn't giving right results also the output array contains extreme values -2147483648 in place of np.nan. Could you verify it?.

– Sandeep Kadapa
Nov 15 '18 at 6:10

@coldspeed : that question is quite different. This question can be solved by much simpler answers.

– rnso
Nov 15 '18 at 6:38

add a comment |

2 Answers
2

active

oldest

votes

Use double transpose with shift:

df = df.T.shift(-1,axis=0).T



print(df)



          A1     A2     A3     A4  A5

#0001    0.0    0.0  777.0  888.0 NaN

#0002  403.0  430.0  320.0   55.0 NaN

#0003  304.0  403.0  403.0  403.0 NaN

#0004  403.0  206.0    NaN  312.0 NaN

#0005  394.0  493.0    NaN  403.0 NaN

Actually df.shift(-1,axis=1) should work but there is bug in the code:

print(df.shift(-1,axis=1))

          A1     A2     A3  A4  A5

#0001  777.0    0.0  888.0 NaN NaN

#0002  320.0  430.0   55.0 NaN NaN

#0003  403.0  403.0  403.0 NaN NaN

#0004    NaN  206.0  312.0 NaN NaN

#0005    NaN  493.0  403.0 NaN NaN

edited Nov 15 '18 at 4:56

answered Nov 15 '18 at 4:46

Sandeep Kadapa

7,218830

1

Simply df.shift(-1, axis=1) shifts on column.

– rushinstuffin
Nov 15 '18 at 4:55

1

@rushinstuffin It doesn't work, there is bug in shift code.

– Sandeep Kadapa
Nov 15 '18 at 4:57

1

Oh, thanks. I didn't realize that. It seems to be because of the NaN values? Without those it seems to work fine.

– rushinstuffin
Nov 15 '18 at 5:06

add a comment |

Try this simple loop:

for i in range(len(df.columns)-1): 

    df.iloc[:,i] = df.iloc[:,i+1]

# for last column:

df[i+1] = np.nan

Output:

        A1   A2     A3   A4  A5

#0001    0    0  777.0  888 NaN

#0002  403  430  320.0   55 NaN

#0003  304  403  403.0  403 NaN

#0004  403  206    NaN  312 NaN

#0005  394  493    NaN  403 NaN

edited Nov 15 '18 at 6:41

answered Nov 15 '18 at 4:56

rnso

11.9k1441101

add a comment |

2 Answers
2

active

oldest

votes

2 Answers
2

active

oldest

votes

Use double transpose with shift:

df = df.T.shift(-1,axis=0).T



print(df)



          A1     A2     A3     A4  A5

#0001    0.0    0.0  777.0  888.0 NaN

#0002  403.0  430.0  320.0   55.0 NaN

#0003  304.0  403.0  403.0  403.0 NaN

#0004  403.0  206.0    NaN  312.0 NaN

#0005  394.0  493.0    NaN  403.0 NaN

Actually df.shift(-1,axis=1) should work but there is bug in the code:

print(df.shift(-1,axis=1))

          A1     A2     A3  A4  A5

#0001  777.0    0.0  888.0 NaN NaN

#0002  320.0  430.0   55.0 NaN NaN

#0003  403.0  403.0  403.0 NaN NaN

#0004    NaN  206.0  312.0 NaN NaN

#0005    NaN  493.0  403.0 NaN NaN

edited Nov 15 '18 at 4:56

answered Nov 15 '18 at 4:46

Sandeep Kadapa

7,218830

1

Simply df.shift(-1, axis=1) shifts on column.

– rushinstuffin
Nov 15 '18 at 4:55

1

@rushinstuffin It doesn't work, there is bug in shift code.

– Sandeep Kadapa
Nov 15 '18 at 4:57

1

Oh, thanks. I didn't realize that. It seems to be because of the NaN values? Without those it seems to work fine.

– rushinstuffin
Nov 15 '18 at 5:06

add a comment |

Use double transpose with shift:

df = df.T.shift(-1,axis=0).T



print(df)



          A1     A2     A3     A4  A5

#0001    0.0    0.0  777.0  888.0 NaN

#0002  403.0  430.0  320.0   55.0 NaN

#0003  304.0  403.0  403.0  403.0 NaN

#0004  403.0  206.0    NaN  312.0 NaN

#0005  394.0  493.0    NaN  403.0 NaN

Actually df.shift(-1,axis=1) should work but there is bug in the code:

print(df.shift(-1,axis=1))

          A1     A2     A3  A4  A5

#0001  777.0    0.0  888.0 NaN NaN

#0002  320.0  430.0   55.0 NaN NaN

#0003  403.0  403.0  403.0 NaN NaN

#0004    NaN  206.0  312.0 NaN NaN

#0005    NaN  493.0  403.0 NaN NaN

edited Nov 15 '18 at 4:56

answered Nov 15 '18 at 4:46

Sandeep Kadapa

7,218830

1

Simply df.shift(-1, axis=1) shifts on column.

– rushinstuffin
Nov 15 '18 at 4:55

1

@rushinstuffin It doesn't work, there is bug in shift code.

– Sandeep Kadapa
Nov 15 '18 at 4:57

1

Oh, thanks. I didn't realize that. It seems to be because of the NaN values? Without those it seems to work fine.

– rushinstuffin
Nov 15 '18 at 5:06

add a comment |

Use double transpose with shift:

df = df.T.shift(-1,axis=0).T



print(df)



          A1     A2     A3     A4  A5

#0001    0.0    0.0  777.0  888.0 NaN

#0002  403.0  430.0  320.0   55.0 NaN

#0003  304.0  403.0  403.0  403.0 NaN

#0004  403.0  206.0    NaN  312.0 NaN

#0005  394.0  493.0    NaN  403.0 NaN

Actually df.shift(-1,axis=1) should work but there is bug in the code:

print(df.shift(-1,axis=1))

          A1     A2     A3  A4  A5

#0001  777.0    0.0  888.0 NaN NaN

#0002  320.0  430.0   55.0 NaN NaN

#0003  403.0  403.0  403.0 NaN NaN

#0004    NaN  206.0  312.0 NaN NaN

#0005    NaN  493.0  403.0 NaN NaN

edited Nov 15 '18 at 4:56

answered Nov 15 '18 at 4:46

Sandeep Kadapa

7,218830

Use double transpose with shift:

df = df.T.shift(-1,axis=0).T



print(df)



          A1     A2     A3     A4  A5

#0001    0.0    0.0  777.0  888.0 NaN

#0002  403.0  430.0  320.0   55.0 NaN

#0003  304.0  403.0  403.0  403.0 NaN

#0004  403.0  206.0    NaN  312.0 NaN

#0005  394.0  493.0    NaN  403.0 NaN

Actually df.shift(-1,axis=1) should work but there is bug in the code:

print(df.shift(-1,axis=1))

          A1     A2     A3  A4  A5

#0001  777.0    0.0  888.0 NaN NaN

#0002  320.0  430.0   55.0 NaN NaN

#0003  403.0  403.0  403.0 NaN NaN

#0004    NaN  206.0  312.0 NaN NaN

#0005    NaN  493.0  403.0 NaN NaN

edited Nov 15 '18 at 4:56

answered Nov 15 '18 at 4:46

Sandeep Kadapa

7,218830

edited Nov 15 '18 at 4:56

answered Nov 15 '18 at 4:46

Sandeep Kadapa

7,218830

answered Nov 15 '18 at 4:46

Sandeep Kadapa

7,218830

answered Nov 15 '18 at 4:46

Sandeep Kadapa

7,218830

1

Simply df.shift(-1, axis=1) shifts on column.

– rushinstuffin
Nov 15 '18 at 4:55

1

@rushinstuffin It doesn't work, there is bug in shift code.

– Sandeep Kadapa
Nov 15 '18 at 4:57

1

Oh, thanks. I didn't realize that. It seems to be because of the NaN values? Without those it seems to work fine.

– rushinstuffin
Nov 15 '18 at 5:06

add a comment |

1

Simply df.shift(-1, axis=1) shifts on column.

– rushinstuffin
Nov 15 '18 at 4:55

1

@rushinstuffin It doesn't work, there is bug in shift code.

– Sandeep Kadapa
Nov 15 '18 at 4:57

1

Oh, thanks. I didn't realize that. It seems to be because of the NaN values? Without those it seems to work fine.

– rushinstuffin
Nov 15 '18 at 5:06

Simply df.shift(-1, axis=1) shifts on column.

– rushinstuffin
Nov 15 '18 at 4:55

@rushinstuffin It doesn't work, there is bug in shift code.

– Sandeep Kadapa
Nov 15 '18 at 4:57

Oh, thanks. I didn't realize that. It seems to be because of the NaN values? Without those it seems to work fine.

– rushinstuffin
Nov 15 '18 at 5:06

add a comment |

Try this simple loop:

for i in range(len(df.columns)-1): 

    df.iloc[:,i] = df.iloc[:,i+1]

# for last column:

df[i+1] = np.nan

Output:

        A1   A2     A3   A4  A5

#0001    0    0  777.0  888 NaN

#0002  403  430  320.0   55 NaN

#0003  304  403  403.0  403 NaN

#0004  403  206    NaN  312 NaN

#0005  394  493    NaN  403 NaN

edited Nov 15 '18 at 6:41

answered Nov 15 '18 at 4:56

rnso

11.9k1441101

add a comment |

Try this simple loop:

for i in range(len(df.columns)-1): 

    df.iloc[:,i] = df.iloc[:,i+1]

# for last column:

df[i+1] = np.nan

Output:

        A1   A2     A3   A4  A5

#0001    0    0  777.0  888 NaN

#0002  403  430  320.0   55 NaN

#0003  304  403  403.0  403 NaN

#0004  403  206    NaN  312 NaN

#0005  394  493    NaN  403 NaN

edited Nov 15 '18 at 6:41

answered Nov 15 '18 at 4:56

rnso

11.9k1441101

add a comment |

Try this simple loop:

for i in range(len(df.columns)-1): 

    df.iloc[:,i] = df.iloc[:,i+1]

# for last column:

df[i+1] = np.nan

Output:

        A1   A2     A3   A4  A5

#0001    0    0  777.0  888 NaN

#0002  403  430  320.0   55 NaN

#0003  304  403  403.0  403 NaN

#0004  403  206    NaN  312 NaN

#0005  394  493    NaN  403 NaN

edited Nov 15 '18 at 6:41

answered Nov 15 '18 at 4:56

rnso

11.9k1441101

Try this simple loop:

for i in range(len(df.columns)-1): 

    df.iloc[:,i] = df.iloc[:,i+1]

# for last column:

df[i+1] = np.nan

Output:

        A1   A2     A3   A4  A5

#0001    0    0  777.0  888 NaN

#0002  403  430  320.0   55 NaN

#0003  304  403  403.0  403 NaN

#0004  403  206    NaN  312 NaN

#0005  394  493    NaN  403 NaN

edited Nov 15 '18 at 6:41

answered Nov 15 '18 at 4:56

rnso

11.9k1441101

edited Nov 15 '18 at 6:41

answered Nov 15 '18 at 4:56

rnso

11.9k1441101

answered Nov 15 '18 at 4:56

rnso

11.9k1441101

answered Nov 15 '18 at 4:56

rnso

11.9k1441101

add a comment |

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