start index at 1 for Pandas DataFrame [duplicate]

Just set the index before writing to CSV.

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

And then write it normally. ​

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 

In my opinion best practice is to set the index with a RangeIndex

import pandas as pd result = pd.DataFrame( {'Count': [83, 19, 20]}, index=pd.RangeIndex(start=1, stop=4, name='index') ) >>> result Count index 1 83 2 19 3 20 

I prefer this, because you can define the range and a possible step and a name for the index in one line.

This worked for me

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

Another way in one line:

df.shift()[1:] 

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.

Following on from TomAugspurger's answer, we could use list comprehension rather than np.arrange(), which removes the requirement for importing the module: numpy. You can use the following instead:

df.index = [i+1 for i in range(len(df))] 

Fork from the original answer, giving some cents:

• if I'm not mistaken, starting from version 0.23, index object is RangeIndex type

From the official doc:

RangeIndex is a memory-saving special case of Int64Index limited to representing monotonic ranges. Using RangeIndex may in some instances improve computing speed.

In case of a huge index range, that makes sense, using the representation of the index, instead of defining the whole index at once (saving memory).

Therefore, an example (using Series, but it applies to DataFrame also):

>>> import pandas as pd >>> >>> countries = ['China', 'India', 'USA'] >>> ds = pd.Series(countries) >>> >>> >>> type(ds.index) <class 'pandas.core.indexes.range.RangeIndex'> >>> ds.index RangeIndex(start=0, stop=3, step=1) >>> >>> ds.index += 1 >>> >>> ds.index RangeIndex(start=1, stop=4, step=1) >>> >>> ds 1 China 2 India 3 USA dtype: object >>> 

As you can see, the increment of the index object, changes the start and stop parameters.

This adds a column that accomplishes what you want

df.insert(0,"Column Name", np.arange(1,len(df)+1)) 

Add ".shift()[1:]" while creating a data frame

data = pd.read_csv(r"C:\Users\user\path\data.csv").shift()[1:]