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```{code-cell} ipython3
:tags: [remove-cell]

%matplotlib inline
```

# Indexing and slicing one-dimensional arrays

Indexing and slicing a one-dimensional array is identical to [indexing](python_lists_indexing.md) and [slicing](python_lists_slicing.md) a Python list.

:::{tip}
As a reminder:

- Indexing means accessing one element using an index: `the_array[2]`
- Slicing means accessing several elements using a slice: `the_array[0:3]`
:::

Throughout this section, we will work with two example arrays:
- `data`: some random data;
- `time`: the corresponding time.

```{code-cell} ipython3
import numpy as np
import matplotlib.pyplot as plt

data = np.array([0.0, 0.58, 0.95, 0.95, 0.58, 0.0, -0.59, -0.96, -0.96, -0.59])
time = np.arange(10) / 10

plt.plot(time, data, "s-")
plt.grid(True);
```

As mentioned above, **indexing** a one-dimensional array is identical to [indexing a list](python_lists_indexing.md). To read one element of a list/array:

```{code-cell} ipython3
data[2]  # Read the 3rd element
```

To write one element of a list/array:

```{code-cell} ipython3
data[2] = 0.58  # Assign 0.35 to the 3rd element

# Plot the result
plt.plot(time, data, "s-")
plt.grid(True)

data[2] = 0.95  # Reset to its original value
```

Negative indexing also works:

```{code-cell} ipython3
# Read the last data
data[-1]
```


**Slicing** a one-dimensional array is identical to [slicing a list](python_lists_slicing.md). To read several elements of a list/array:

```{code-cell} ipython3
# Keep only the values at indexes 2, 4, 6 and 8 of the original array
# Start at 2 (incl.), up to 9 (excl.) by steps of 2:
data_subset = data[2:9:2]
time_subset = time[2:9:2]

# Plot the result
plt.plot(time, data, "s-", label="original data")
plt.plot(time_subset, data_subset, "o-", label="subset")
plt.grid(True)
plt.legend();
```

To write several elements of a list/array:

```{code-cell} ipython3
# Replace values at indexes 2, 4, 6, 8 of the original list/array by
# [-0.7, -0.8, -0.9, -1.0]

modified_data = data.copy()
modified_data[2:9:2] = [-0.7, -0.8, -0.9, -1.0]

# Plot the result
plt.plot(time, data, "s-", label="original data")
plt.plot(time, modified_data, "o-", label="modified data")
plt.grid(True)
plt.legend();
```


:::{tip}
Note the line:

```
modified_data = data.copy()
```

Using the {{ndarray_copy}} method is required here, because otherwise we are just telling Python to assign an additional name (`modified_data`) to the same variable (`data`). In this case, modifying `modified_data` would also modify `data` since they are both the same array. Using the `copy` method creates a new, unique array so that modifying one won't modify the other.
:::