# 23. Introduction to Pandas

By **Bernd Klein**. Last modified: 23 Nov 2021.

The pandas we are writing about in this chapter have nothing to do with the cute panda bears. Endearing bears are not what our visitors expect in a Python tutorial. Pandas is the name for a Python module, which is rounding up the capabilities of Numpy, Scipy and Matplotlab. The word pandas is an acronym which is derived from "Python and data analysis" and "panel data".

There is often some confusion about whether Pandas is an alternative to Numpy, SciPy and Matplotlib. The truth is that it is built on top of Numpy. This means that Numpy is required by pandas. Scipy and Matplotlib on the other hand are not required by pandas but they are extremely useful. That's why the Pandas project lists them as "optional dependency".

Pandas is a software library written for the Python programming language. It is used for data manipulation and analysis. It provides special data structures and operations for the manipulation of numerical tables and time series. Pandas is free software released under the three-clause BSD license.

## Data Structures

We will start with the following two important data structures of Pandas:

- Series and
- DataFrame

## Series

A Series is a one-dimensional labelled array-like object. It is capable of holding any data type, e.g. integers, floats, strings, Python objects, and so on. It can be seen as a data structure with two arrays: one functioning as the index, i.e. the labels, and the other one contains the actual data.

We define a simple Series object in the following example by instantiating a Pandas Series object with a list. We will later see that we can use other data objects for example Numpy arrays and dictionaries as well to instantiate a Series object.

```
import pandas as pd
S = pd.Series([11, 28, 72, 3, 5, 8])
S
```

### OUTPUT:

0 11 1 28 2 72 3 3 4 5 5 8 dtype: int64

We haven't defined an index in our example, but we see two columns in our output: The right column contains our data, whereas the left column contains the index. Pandas created a default index starting with 0 going to 5, which is the length of the data minus 1.

We can directly access the index and the values of our Series S:

```
print(S.index)
print(S.values)
```

### OUTPUT:

RangeIndex(start=0, stop=6, step=1) [11 28 72 3 5 8]

If we compare this to creating an array in numpy, we will find lots of similarities:

```
import numpy as np
X = np.array([11, 28, 72, 3, 5, 8])
print(X)
print(S.values)
# both are the same type:
print(type(S.values), type(X))
```

### OUTPUT:

[11 28 72 3 5 8] [11 28 72 3 5 8] <class 'numpy.ndarray'> <class 'numpy.ndarray'>

So far our Series have not been very different to ndarrays of Numpy. This changes, as soon as we start defining Series objects with individual indices:

```
fruits = ['apples', 'oranges', 'cherries', 'pears']
quantities = [20, 33, 52, 10]
S = pd.Series(quantities, index=fruits)
S
```

### OUTPUT:

apples 20 oranges 33 cherries 52 pears 10 dtype: int64

A big advantage to NumPy arrays is obvious from the previous example: We can use arbitrary indices.

If we add two series with the same indices, we get a new series with the same index and the correponding values will be added:

```
fruits = ['apples', 'oranges', 'cherries', 'pears']
S = pd.Series([20, 33, 52, 10], index=fruits)
S2 = pd.Series([17, 13, 31, 32], index=fruits)
print(S + S2)
print("sum of S: ", sum(S))
```

### OUTPUT:

apples 37 oranges 46 cherries 83 pears 42 dtype: int64 sum of S: 115

The indices do not have to be the same for the Series addition. The index will be the "union" of both indices. If an index doesn't occur in both Series, the value for this Series will be NaN:

```
fruits = ['peaches', 'oranges', 'cherries', 'pears']
fruits2 = ['raspberries', 'oranges', 'cherries', 'pears']
S = pd.Series([20, 33, 52, 10], index=fruits)
S2 = pd.Series([17, 13, 31, 32], index=fruits2)
print(S + S2)
```

### OUTPUT:

cherries 83.0 oranges 46.0 peaches NaN pears 42.0 raspberries NaN dtype: float64

In principle, the indices can be completely different, as in the following example. We have two indices. One is the Turkish translation of the English fruit names:

```
fruits = ['apples', 'oranges', 'cherries', 'pears']
fruits_tr = ['elma', 'portakal', 'kiraz', 'armut']
S = pd.Series([20, 33, 52, 10], index=fruits)
S2 = pd.Series([17, 13, 31, 32], index=fruits_tr)
print(S + S2)
```

### OUTPUT:

apples NaN armut NaN cherries NaN elma NaN kiraz NaN oranges NaN pears NaN portakal NaN dtype: float64

### Indexing

It's possible to access single values of a Series.

```
print(S['apples'])
```

### OUTPUT:

20

This looks like accessing the values of dictionaries through keys.

However, Series objects can also be accessed by multiple indexes at the same time. This can be done by packing the indexes into a list. This type of access returns a Pandas Series again:

```
print(S[['apples', 'oranges', 'cherries']])
```

### OUTPUT:

apples 20 oranges 33 cherries 52 dtype: int64

Similar to Numpy we can use scalar operations or mathematical functions on a series:

```
import numpy as np
print((S + 3) * 4)
print("======================")
print(np.sin(S))
```

### OUTPUT:

apples 92 oranges 144 cherries 220 pears 52 dtype: int64 ====================== apples 0.912945 oranges 0.999912 cherries 0.986628 pears -0.544021 dtype: float64

### pandas.Series.apply

Series.apply(func, convert_dtype=True, args=(), **kwds)

The function "func" will be applied to the Series and it returns either a Series or a DataFrame, depending on "func".

Parameter | Meaning |
---|---|

func | a function, which can be a NumPy function that will be applied to the entire Series or a Python function that will be applied to every single value of the series |

convert_dtype | A boolean value. If it is set to True (default), apply will try to find better dtype for elementwise function results. If False, leave as dtype=object |

args | Positional arguments which will be passed to the function "func" additionally to the values from the series. |

**kwds | Additional keyword arguments will be passed as keywords to the function |

Example:

```
S.apply(np.log)
```

### OUTPUT:

apples 2.995732 oranges 3.496508 cherries 3.951244 pears 2.302585 dtype: float64

We can also use Python lambda functions. Let's assume, we have the following task. The test the amount of fruit for every kind. If there are less than 50 available, we will augment the stock by 10:

```
S.apply(lambda x: x if x > 50 else x+10 )
```

### OUTPUT:

apples 30 oranges 43 cherries 52 pears 20 dtype: int64

### Filtering with a Boolean array

Similar to numpy arrays, we can filter Pandas Series with a Boolean array:

```
S[S>30]
```

### OUTPUT:

oranges 33 cherries 52 dtype: int64

A series can be seen as an ordered Python dictionary with a fixed length.

```
"apples" in S
```

### OUTPUT:

True

### Creating Series Objects from Dictionaries

We can even use a dictionary to create a Series object. The resulting Series contains the dict's keys as the indices and the values as the values.

```
cities = {"London": 8615246,
"Berlin": 3562166,
"Madrid": 3165235,
"Rome": 2874038,
"Paris": 2273305,
"Vienna": 1805681,
"Bucharest": 1803425,
"Hamburg": 1760433,
"Budapest": 1754000,
"Warsaw": 1740119,
"Barcelona": 1602386,
"Munich": 1493900,
"Milan": 1350680}
city_series = pd.Series(cities)
print(city_series)
```

### OUTPUT:

London 8615246 Berlin 3562166 Madrid 3165235 Rome 2874038 Paris 2273305 Vienna 1805681 Bucharest 1803425 Hamburg 1760433 Budapest 1754000 Warsaw 1740119 Barcelona 1602386 Munich 1493900 Milan 1350680 dtype: int64

## NaN - Missing Data

One problem in dealing with data analysis tasks consists in missing data. Pandas makes it as easy as possible to work with missing data.

If we look once more at our previous example, we can see that the index of our series is the same as the keys of the dictionary we used to create the cities_series. Now, we want to use an index which is not overlapping with the dictionary keys. We have already seen that we can pass a list or a tuple to the keyword argument 'index' to define the index. In our next example, the list (or tuple) passed to the keyword parameter 'index' will not be equal to the keys. This means that some cities from the dictionary will be missing and two cities ("Zurich" and "Stuttgart") don't occur in the dictionary.

```
my_cities = ["London", "Paris", "Zurich", "Berlin",
"Stuttgart", "Hamburg"]
my_city_series = pd.Series(cities,
index=my_cities)
my_city_series
```

### OUTPUT:

London 8615246.0 Paris 2273305.0 Zurich NaN Berlin 3562166.0 Stuttgart NaN Hamburg 1760433.0 dtype: float64

Due to the Nan values the population values for the other cities are turned into floats. There is no missing data in the following examples, so the values are int:

```
my_cities = ["London", "Paris", "Berlin", "Hamburg"]
my_city_series = pd.Series(cities,
index=my_cities)
my_city_series
```

### OUTPUT:

London 8615246 Paris 2273305 Berlin 3562166 Hamburg 1760433 dtype: int64

### The Methods isnull() and notnull()

We can see, that the cities, which are not included in the dictionary, get the value NaN assigned. NaN stands for "not a number". It can also be seen as meaning "missing" in our example.

We can check for missing values with the methods isnull and notnull:

```
my_cities = ["London", "Paris", "Zurich", "Berlin",
"Stuttgart", "Hamburg"]
my_city_series = pd.Series(cities,
index=my_cities)
print(my_city_series.isnull())
```

### OUTPUT:

London False Paris False Zurich True Berlin False Stuttgart True Hamburg False dtype: bool

```
print(my_city_series.notnull())
```

### OUTPUT:

London True Paris True Zurich False Berlin True Stuttgart False Hamburg True dtype: bool

### Connection between NaN and None

We get also a NaN, if a value in the dictionary has a None:

```
d = {"a":23, "b":45, "c":None, "d":0}
S = pd.Series(d)
print(S)
```

### OUTPUT:

a 23.0 b 45.0 c NaN d 0.0 dtype: float64

```
pd.isnull(S)
```

### OUTPUT:

a False b False c True d False dtype: bool

```
pd.notnull(S)
```

### OUTPUT:

a True b True c False d True dtype: bool

### Filtering out Missing Data

It's possible to filter out missing data with the Series method dropna. It returns a Series which consists only of non-null data:

```
print(my_city_series.dropna())
```

### OUTPUT:

London 8615246.0 Paris 2273305.0 Berlin 3562166.0 Hamburg 1760433.0 dtype: float64

### Filling in Missing Data

In many cases you don't want to filter out missing data, but you want to fill in appropriate data for the empty gaps. A suitable method in many situations will be fillna:

```
print(my_city_series.fillna(0))
```

### OUTPUT:

London 8615246.0 Paris 2273305.0 Zurich 0.0 Berlin 3562166.0 Stuttgart 0.0 Hamburg 1760433.0 dtype: float64

Okay, that's not what we call "fill in appropriate data for the empty gaps". If we call fillna with a dict, we can provide the appropriate data, i.e. the population of Zurich and Stuttgart:

```
missing_cities = {"Stuttgart":597939, "Zurich":378884}
my_city_series.fillna(missing_cities)
```

### OUTPUT:

London 8615246.0 Paris 2273305.0 Zurich 378884.0 Berlin 3562166.0 Stuttgart 597939.0 Hamburg 1760433.0 dtype: float64

We still have the problem that integer values - which means values which should be integers like number of people - are converted to float as soon as we have NaN values. We can solve this problem now with the method 'fillna':

```
cities = {"London": 8615246,
"Berlin": 3562166,
"Madrid": 3165235,
"Rome": 2874038,
"Paris": 2273305,
"Vienna": 1805681,
"Bucharest":1803425,
"Hamburg": 1760433,
"Budapest": 1754000,
"Warsaw": 1740119,
"Barcelona":1602386,
"Munich": 1493900,
"Milan": 1350680}
my_cities = ["London", "Paris", "Zurich", "Berlin",
"Stuttgart", "Hamburg"]
my_city_series = pd.Series(cities,
index=my_cities)
my_city_series = my_city_series.fillna(0).astype(int)
print(my_city_series)
```

### OUTPUT:

London 8615246 Paris 2273305 Zurich 0 Berlin 3562166 Stuttgart 0 Hamburg 1760433 dtype: int64