# Add third dimension to a 2-dimensional numpy.ndarray

I have an array which contains 50 time series. Each time series has 50 values.
The shape of my array is therefore:

``````print(arr.shape) = (50,50)
``````

I want to extract the 50 time series and I want to assign a year to each of them:

``````years = list(range(1900,1950))
print(len(years)) = 50
``````

The order should be maintained. `years` should correspond with `arr[0,:]` (this is the first time series).

I am glad for any help!

Edit: This is the small example

``````import random

years = list(range(1900,1904))
values = random.sample(range(10, 30), 16)
arr = np.reshape(values, (4, 4))
``````

### >Solution :

Let’s say you have the following data:

``````import numpy as np

data = np.random.randint(low=1, high=9, size=(5, 4))
years = np.arange(1900, 1905)
``````

You can use `np.concatenate`:

``````>>> arr = np.concatenate([years[:, None], data], axis=1)
>>> arr

array([[1900,    5,    8,    1,    2],
[1901,    3,    3,    1,    5],
[1902,    7,    4,    7,    5],
[1903,    1,    6,    6,    4],
[1904,    4,    5,    3,    8]])
``````

or maybe use a `pandas.DataFrame`:

``````>>> import pandas as pd

>>> df = pd.DataFrame(data)
>>> df = df.assign(year=years)
>>> df = df.set_index("year")
>>> df

0  1  2  3
year
1900  3  2  8  1
1901  5  8  5  2
1902  3  5  4  3
1903  6  2  7  6
1904  8  8  4  6
``````