Start index at 1 when writing Pandas DataFrame to CSV

I need the index to start at 1 rather than 0 when writing a Pandas DataFrame to CSV. Here's an example:

In [1]: import pandas as pd



In [2]: result = pd.DataFrame({'Count': [83, 19, 20]})



In [3]: result.to_csv('result.csv', index_label='Event_id')

Which produces the following output:

In [4]: !cat result.csv

Event_id,Count

0,83

1,19

2,20

But my desired output is this:

In [5]: !cat result2.csv

Event_id,Count

1,83

2,19

3,20

I realize that this could be done by adding a sequence of integers shifted by 1 as a column to my data frame, but I'm new to Pandas and wondering if a cleaner way exists.

Thanks.

asked Nov 23 '13 at 21:12

Clark Fitzgerald

140127

add a comment |

I need the index to start at 1 rather than 0 when writing a Pandas DataFrame to CSV. Here's an example:

In [1]: import pandas as pd



In [2]: result = pd.DataFrame({'Count': [83, 19, 20]})



In [3]: result.to_csv('result.csv', index_label='Event_id')

Which produces the following output:

In [4]: !cat result.csv

Event_id,Count

0,83

1,19

2,20

But my desired output is this:

In [5]: !cat result2.csv

Event_id,Count

1,83

2,19

3,20

I realize that this could be done by adding a sequence of integers shifted by 1 as a column to my data frame, but I'm new to Pandas and wondering if a cleaner way exists.

Thanks.

asked Nov 23 '13 at 21:12

Clark Fitzgerald

140127

add a comment |

I need the index to start at 1 rather than 0 when writing a Pandas DataFrame to CSV. Here's an example:

In [1]: import pandas as pd



In [2]: result = pd.DataFrame({'Count': [83, 19, 20]})



In [3]: result.to_csv('result.csv', index_label='Event_id')

Which produces the following output:

In [4]: !cat result.csv

Event_id,Count

0,83

1,19

2,20

But my desired output is this:

In [5]: !cat result2.csv

Event_id,Count

1,83

2,19

3,20

I realize that this could be done by adding a sequence of integers shifted by 1 as a column to my data frame, but I'm new to Pandas and wondering if a cleaner way exists.

Thanks.

asked Nov 23 '13 at 21:12

Clark Fitzgerald

140127

I need the index to start at 1 rather than 0 when writing a Pandas DataFrame to CSV. Here's an example:

In [1]: import pandas as pd



In [2]: result = pd.DataFrame({'Count': [83, 19, 20]})



In [3]: result.to_csv('result.csv', index_label='Event_id')

Which produces the following output:

In [4]: !cat result.csv

Event_id,Count

0,83

1,19

2,20

But my desired output is this:

In [5]: !cat result2.csv

Event_id,Count

1,83

2,19

3,20

I realize that this could be done by adding a sequence of integers shifted by 1 as a column to my data frame, but I'm new to Pandas and wondering if a cleaner way exists.

Thanks.

python csv pandas

asked Nov 23 '13 at 21:12

Clark Fitzgerald

140127

asked Nov 23 '13 at 21:12

Clark Fitzgerald

140127

asked Nov 23 '13 at 21:12

Clark Fitzgerald

140127

asked Nov 23 '13 at 21:12

Clark Fitzgerald

140127

asked Nov 23 '13 at 21:12

Clark Fitzgerald

140127

add a comment |

6 Answers
6

active

oldest

votes

Index is an object, and default index starts from 0:

>>> result.index

Int64Index([0, 1, 2], dtype=int64)

You can shift this index by 1 with

>>> result.index += 1 

>>> result.index

Int64Index([1, 2, 3], dtype=int64)

answered Nov 23 '13 at 21:57

alko

28.2k46886

2

somehow it changes index name - so proper order with naming is: df.index+=1;df.index.name='name'
– yourstruly
Jul 10 '16 at 16:11

add a comment |

Just set the index before writing to csv. df.index = np.arange(1, len(df))

And then write it normally.

answered Nov 23 '13 at 21:54

TomAugspurger

15k35055

where np is import like so: import numpy as np
– Dung
Aug 29 '16 at 21:22

it should be df.index = arange( 1, len(df) + 1)
– Natesh bhat
Jun 4 '18 at 4:47

add a comment |

This worked for me

 df.index = np.arange(1, len(df)+1)

answered Apr 28 '18 at 7:06

Liu Yu

698

add a comment |

source: In Python pandas, start row index from 1 instead of zero without creating additional column

Working example:

import pandas as pdas

dframe = pdas.read_csv(open(input_file))

dframe.index = dframe.index + 1

edited May 23 '17 at 12:34

Community♦

answered Aug 29 '16 at 21:11

Dung

9,31363337

add a comment |

Another way in one line:

df.shift()[1:]

answered Aug 25 '17 at 14:06

Imran

151210

add a comment |

You can use this one:

import pandas as pd



result = pd.DataFrame({'Count': [83, 19, 20]})

result.index += 1

print(result)

or this one, by getting the help of numpy library like this:

import pandas as pd

import numpy as np



result = pd.DataFrame({'Count': [83, 19, 20]})

result.index = np.arange(1, len(result)+1)

print(result)

np.arange will create a numpy array and return values within a given interval which is (1, len(result)+1) and finally you will assign that array to result.index.

answered Nov 23 '18 at 11:00

Utku

691311

add a comment |

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6 Answers
6

active

oldest

votes

6 Answers
6

active

oldest

votes

Index is an object, and default index starts from 0:

>>> result.index

Int64Index([0, 1, 2], dtype=int64)

You can shift this index by 1 with

>>> result.index += 1 

>>> result.index

Int64Index([1, 2, 3], dtype=int64)

answered Nov 23 '13 at 21:57

alko

28.2k46886

2

somehow it changes index name - so proper order with naming is: df.index+=1;df.index.name='name'
– yourstruly
Jul 10 '16 at 16:11

add a comment |

Index is an object, and default index starts from 0:

>>> result.index

Int64Index([0, 1, 2], dtype=int64)

You can shift this index by 1 with

>>> result.index += 1 

>>> result.index

Int64Index([1, 2, 3], dtype=int64)

answered Nov 23 '13 at 21:57

alko

28.2k46886

2

somehow it changes index name - so proper order with naming is: df.index+=1;df.index.name='name'
– yourstruly
Jul 10 '16 at 16:11

add a comment |

Index is an object, and default index starts from 0:

>>> result.index

Int64Index([0, 1, 2], dtype=int64)

You can shift this index by 1 with

>>> result.index += 1 

>>> result.index

Int64Index([1, 2, 3], dtype=int64)

answered Nov 23 '13 at 21:57

alko

28.2k46886

Index is an object, and default index starts from 0:

>>> result.index

Int64Index([0, 1, 2], dtype=int64)

You can shift this index by 1 with

>>> result.index += 1 

>>> result.index

Int64Index([1, 2, 3], dtype=int64)

answered Nov 23 '13 at 21:57

alko

28.2k46886

answered Nov 23 '13 at 21:57

alko

28.2k46886

answered Nov 23 '13 at 21:57

alko

28.2k46886

answered Nov 23 '13 at 21:57

alko

28.2k46886

2

somehow it changes index name - so proper order with naming is: df.index+=1;df.index.name='name'
– yourstruly
Jul 10 '16 at 16:11

add a comment |

2

somehow it changes index name - so proper order with naming is: df.index+=1;df.index.name='name'
– yourstruly
Jul 10 '16 at 16:11

somehow it changes index name - so proper order with naming is: df.index+=1;df.index.name='name'
– yourstruly
Jul 10 '16 at 16:11

add a comment |

Just set the index before writing to csv. df.index = np.arange(1, len(df))

And then write it normally.

answered Nov 23 '13 at 21:54

TomAugspurger

15k35055

where np is import like so: import numpy as np
– Dung
Aug 29 '16 at 21:22

it should be df.index = arange( 1, len(df) + 1)
– Natesh bhat
Jun 4 '18 at 4:47

add a comment |

Just set the index before writing to csv. df.index = np.arange(1, len(df))

And then write it normally.

answered Nov 23 '13 at 21:54

TomAugspurger

15k35055

where np is import like so: import numpy as np
– Dung
Aug 29 '16 at 21:22

it should be df.index = arange( 1, len(df) + 1)
– Natesh bhat
Jun 4 '18 at 4:47

add a comment |

Just set the index before writing to csv. df.index = np.arange(1, len(df))

And then write it normally.

answered Nov 23 '13 at 21:54

TomAugspurger

15k35055

Just set the index before writing to csv. df.index = np.arange(1, len(df))

And then write it normally.

answered Nov 23 '13 at 21:54

TomAugspurger

15k35055

answered Nov 23 '13 at 21:54

TomAugspurger

15k35055

answered Nov 23 '13 at 21:54

TomAugspurger

15k35055

answered Nov 23 '13 at 21:54

TomAugspurger

15k35055

where np is import like so: import numpy as np
– Dung
Aug 29 '16 at 21:22

it should be df.index = arange( 1, len(df) + 1)
– Natesh bhat
Jun 4 '18 at 4:47

add a comment |

where np is import like so: import numpy as np
– Dung
Aug 29 '16 at 21:22

it should be df.index = arange( 1, len(df) + 1)
– Natesh bhat
Jun 4 '18 at 4:47

where np is import like so: import numpy as np
– Dung
Aug 29 '16 at 21:22

it should be df.index = arange( 1, len(df) + 1)
– Natesh bhat
Jun 4 '18 at 4:47

add a comment |

This worked for me

 df.index = np.arange(1, len(df)+1)

answered Apr 28 '18 at 7:06

Liu Yu

698

add a comment |

This worked for me

 df.index = np.arange(1, len(df)+1)

answered Apr 28 '18 at 7:06

Liu Yu

698

add a comment |

This worked for me

 df.index = np.arange(1, len(df)+1)

answered Apr 28 '18 at 7:06

Liu Yu

698

This worked for me

 df.index = np.arange(1, len(df)+1)

answered Apr 28 '18 at 7:06

Liu Yu

698

answered Apr 28 '18 at 7:06

Liu Yu

698

answered Apr 28 '18 at 7:06

Liu Yu

698

answered Apr 28 '18 at 7:06

Liu Yu

698

add a comment |

source: In Python pandas, start row index from 1 instead of zero without creating additional column

Working example:

import pandas as pdas

dframe = pdas.read_csv(open(input_file))

dframe.index = dframe.index + 1

edited May 23 '17 at 12:34

Community♦

answered Aug 29 '16 at 21:11

Dung

9,31363337

add a comment |

source: In Python pandas, start row index from 1 instead of zero without creating additional column

Working example:

import pandas as pdas

dframe = pdas.read_csv(open(input_file))

dframe.index = dframe.index + 1

edited May 23 '17 at 12:34

Community♦

answered Aug 29 '16 at 21:11

Dung

9,31363337

add a comment |

source: In Python pandas, start row index from 1 instead of zero without creating additional column

Working example:

import pandas as pdas

dframe = pdas.read_csv(open(input_file))

dframe.index = dframe.index + 1

edited May 23 '17 at 12:34

Community♦

answered Aug 29 '16 at 21:11

Dung

9,31363337

source: In Python pandas, start row index from 1 instead of zero without creating additional column

Working example:

import pandas as pdas

dframe = pdas.read_csv(open(input_file))

dframe.index = dframe.index + 1

edited May 23 '17 at 12:34

Community♦

answered Aug 29 '16 at 21:11

Dung

9,31363337

edited May 23 '17 at 12:34

Community♦

edited May 23 '17 at 12:34

Community♦

edited May 23 '17 at 12:34

Community♦

answered Aug 29 '16 at 21:11

Dung

9,31363337

answered Aug 29 '16 at 21:11

Dung

9,31363337

answered Aug 29 '16 at 21:11

Dung

9,31363337

add a comment |

Another way in one line:

df.shift()[1:]

answered Aug 25 '17 at 14:06

Imran

151210

add a comment |

Another way in one line:

df.shift()[1:]

answered Aug 25 '17 at 14:06

Imran

151210

add a comment |

Another way in one line:

df.shift()[1:]

answered Aug 25 '17 at 14:06

Imran

151210

Another way in one line:

df.shift()[1:]

answered Aug 25 '17 at 14:06

Imran

151210

answered Aug 25 '17 at 14:06

Imran

151210

answered Aug 25 '17 at 14:06

Imran

151210

answered Aug 25 '17 at 14:06

Imran

151210

add a comment |

You can use this one:

import pandas as pd



result = pd.DataFrame({'Count': [83, 19, 20]})

result.index += 1

print(result)

or this one, by getting the help of numpy library like this:

import pandas as pd

import numpy as np



result = pd.DataFrame({'Count': [83, 19, 20]})

result.index = np.arange(1, len(result)+1)

print(result)

np.arange will create a numpy array and return values within a given interval which is (1, len(result)+1) and finally you will assign that array to result.index.

answered Nov 23 '18 at 11:00

Utku

691311

add a comment |

You can use this one:

import pandas as pd



result = pd.DataFrame({'Count': [83, 19, 20]})

result.index += 1

print(result)

or this one, by getting the help of numpy library like this:

import pandas as pd

import numpy as np



result = pd.DataFrame({'Count': [83, 19, 20]})

result.index = np.arange(1, len(result)+1)

print(result)

np.arange will create a numpy array and return values within a given interval which is (1, len(result)+1) and finally you will assign that array to result.index.

answered Nov 23 '18 at 11:00

Utku

691311

add a comment |

You can use this one:

import pandas as pd



result = pd.DataFrame({'Count': [83, 19, 20]})

result.index += 1

print(result)

or this one, by getting the help of numpy library like this:

import pandas as pd

import numpy as np



result = pd.DataFrame({'Count': [83, 19, 20]})

result.index = np.arange(1, len(result)+1)

print(result)

np.arange will create a numpy array and return values within a given interval which is (1, len(result)+1) and finally you will assign that array to result.index.

answered Nov 23 '18 at 11:00

Utku

691311

You can use this one:

import pandas as pd



result = pd.DataFrame({'Count': [83, 19, 20]})

result.index += 1

print(result)

or this one, by getting the help of numpy library like this:

import pandas as pd

import numpy as np



result = pd.DataFrame({'Count': [83, 19, 20]})

result.index = np.arange(1, len(result)+1)

print(result)

np.arange will create a numpy array and return values within a given interval which is (1, len(result)+1) and finally you will assign that array to result.index.

answered Nov 23 '18 at 11:00

Utku

691311

answered Nov 23 '18 at 11:00

Utku

691311

answered Nov 23 '18 at 11:00

Utku

691311

answered Nov 23 '18 at 11:00

Utku

691311

add a comment |

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