Change the Index Number of a Dataframe

How do I change a single index value in pandas dataframe?

You want to do something like this:

as_list = df.index.tolist()
idx = as_list.index('Republic of Korea')
as_list[idx] = 'South Korea'
df.index = as_list

Basically, you get the index as a list, change that one element, and the replace the existing index.

How to convert index of a pandas dataframe into a column

either:

df['index1'] = df.index

or, .reset_index:

df = df.reset_index(level=0)

so, if you have a multi-index frame with 3 levels of index, like:

>>> df
                       val
tick       tag obs        
2016-02-26 C   2    0.0139
2016-02-27 A   2    0.5577
2016-02-28 C   6    0.0303

and you want to convert the 1st (tick) and 3rd (obs) levels in the index into columns, you would do:

>>> df.reset_index(level=['tick', 'obs'])
          tick  obs     val
tag                        
C   2016-02-26    2  0.0139
A   2016-02-27    2  0.5577
C   2016-02-28    6  0.0303

Change number format in dataframe index

You can convert your indexes values to int, but you must do it carefully, cause you can loose some ids:

df = pd.DataFrame({'PaperId':[1000000000.0, 2.0, 3.0, 4.0], 
                      'memberNum':[1, 2, 3, 4]})

df = df.set_index('PaperId')
df

            memberNum
PaperId 
1.000000e+09    1
2.000000e+00    2
3.000000e+00    3
4.000000e+00    4

df['PaperId'] = df.index
df['PaperId'] = df['PaperId'].astype('int')
df = df.set_index('PaperId')
df

        memberNum
PaperId 
1000000000  1
2           2
3           3
4           4

Change the index number of a dataframe

These are the rownames of your dataframe, which by default are 1:nrow(dfr). When you reordered the dataframe, the original rownames are also reordered. To have the rows of the new order listed sequentially, just use:

rownames(dfr) <- 1:nrow(dfr)

Pandas - Replace values based on index

Use loc:

df.loc[0:15,'A'] = 16
print (df)
     A   B
0   16  45
1   16   5
2   16  97
3   16  58
4   16  26
5   16  87
6   16  51
7   16  17
8   16  39
9   16  73
10  16  94
11  16  69
12  16  57
13  16  24
14  16  43
15  16  77
16  41   0
17   3  21
18   0  98
19  45  39
20  66  62
21   8  53
22  69  47
23  48  53

Solution with ix is deprecated.

python: changing row index of pandas data frame

you can do

followers_df.index = range(20)

Is there an easier way to change the index values of a pandas dataframe?

try:

test = test.reset_index(drop=True).rename_axis('row_id')

Set value for particular cell in pandas DataFrame using index

RukTech's answer, df.set_value('C', 'x', 10), is far and away faster than the options I've suggested below. However, it has been slated for deprecation.

Going forward, the recommended method is .iat/.at.

Why df.xs('C')['x']=10 does not work:

df.xs('C') by default, returns a new dataframe with a copy of the data, so

df.xs('C')['x']=10

modifies this new dataframe only.

df['x'] returns a view of the df dataframe, so

df['x']['C'] = 10

modifies df itself.

Warning: It is sometimes difficult to predict if an operation returns a copy or a view. For this reason the docs recommend avoiding assignments with "chained indexing".

So the recommended alternative is

df.at['C', 'x'] = 10

which does modify df.

In [18]: %timeit df.set_value('C', 'x', 10)
100000 loops, best of 3: 2.9 µs per loop

In [20]: %timeit df['x']['C'] = 10
100000 loops, best of 3: 6.31 µs per loop

In [81]: %timeit df.at['C', 'x'] = 10
100000 loops, best of 3: 9.2 µs per loop

Change all index values of pandas dataframe to a range

You can convert your index to a pandas Categorical and then substitute your index by the categorical's codes:

df.index = pd.Categorical(df.index).codes