# Importing Scientific Data using Pandas

[Introduction text]


Pandas is a library that is built on top of NumPy and is therefore customarily to import both when working with Pandas.

```python
import numpy as np
import pandas as pd
```

---
DataFrame

A `DataFrame` in pandas is a two-dimensional data structure like table with rows and columns. We can either load some data in a [python dictionary:](https://www.w3schools.com/python/python_dictionaries.asp)

```python
# Tensile test data
data = {
    "Force (N)": [10, 15, 20, 25],
    "Displacement (mm)": [1.2, 1.8, 2.5, 3.0],
    "Stress (MPa)": [2.6, 3.9, 5.2, 6.5],
}
```

Or we can load a spreadsheet in the form of a CSV file with the `read_csv()` function. In order to do this we need 

```python
# Read CSV file into a DataFrame
pd.read_csv("data.csv")
```

---
Alternatively, the `read_excel()` function allows you to import xlsx files. For this you need to specify the sheet name.

```python
df.read_excel("foo.xlsx", sheet_name="Sheet1", index_col=None, na_values=["NA"])
```
---

Object Creation

Up to this point, the data has been read into memory but not assigned to a variable. By convention, we will assign the resulting DataFrame to the variable `df`, where the name `df` serves as a common shorthand for “data frame.”

```python
# Create DataFrame with row labels
df = pd.DataFrame(data, index=["Test1", "Test2", "Test3", "Test4"])

print(df)
```

Creating the object allows us to re-call the data or specific data points if needed.

---

Calling Data

Data can be called using the square brackets. We can get an entire column or row using it's label as follows:

Single point:
```python
df[3,2]
```

Ranges:
```python
df[2:4,2:3]
```

Data series by label
```python
df["Test1"]
```

---


Assignment 1:


```python

```

---

Assignment 2:
Load a the data from the csv file and calculate the mean point of the array.

```python

```