import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os

pd.set_option('display.max_rows', 9)
plt.style.use('ggplot')

Lecture 3 – More DataFrame Fundamentals

DSC 80, Spring 2023

Agenda

• Recap: loc and iloc.
• Adding and modifying columns.
• Axes.
• pandas and numpy.
• Extra: Data cleaning and plotly.

Recap: loc and iloc

Example: Universities in California 📚

Recall, last lecture we started working with a dataset that contains the name, location, enrollment, and founding date of most UCs and CSUs.

schools_path = os.path.join('data', 'california_universities.csv')
schools = pd.read_csv(schools_path)
schools.head()

	Name	City	County	Enrollment	Founded
0	Humboldt State University	Arcata	Humboldt	7,774	1913
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002
4	California State University, Dominguez Hills	Carson	Los Angeles	15,741	1960

loc and iloc with the default index

• We use loc to access rows by their indexes (labels).
• We use iloc to access rows by their integer positions.
• When we load a DataFrame from file, the default index is 0, 1, 2, 3, ...
• In some cases, loc and iloc behave similarly – but they are not the same!

schools.head()

	Name	City	County	Enrollment	Founded
0	Humboldt State University	Arcata	Humboldt	7,774	1913
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002
4	California State University, Dominguez Hills	Carson	Los Angeles	15,741	1960

What's the difference between the two DataFrames below?

schools.loc[1:5]

	Name	City	County	Enrollment	Founded
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002
4	California State University, Dominguez Hills	Carson	Los Angeles	15,741	1960
5	California State University, Chico	Chico	Butte	17,488	1887

schools.iloc[1:5]

	Name	City	County	Enrollment	Founded
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002
4	California State University, Dominguez Hills	Carson	Los Angeles	15,741	1960

Which of the following two expressions evaluate to the name of the youngest school in schools?

schools.sort_values('Founded', ascending=False).iloc[0]['Name']

'University of California, Merced'

schools.sort_values('Founded', ascending=False).loc[0]['Name']

'Humboldt State University'

Adding and modifying columns

Adding and modifying columns, using a copy

• To add a new column to a DataFrame, use the assign method.
  • To change the values in a column, add a new column with the same name as the existing column.
• Like most pandas methods, assign returns a new DataFrame.
  • Pro ✅: This doesn't inadvertently change any existing variables.
  • Con ❌: It is not very space efficient, as it creates a new copy each time it is called.

schools.head()

	Name	City	County	Enrollment	Founded
0	Humboldt State University	Arcata	Humboldt	7,774	1913
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002
4	California State University, Dominguez Hills	Carson	Los Angeles	15,741	1960

schools.assign(Age=2023 - schools['Founded'])

	Name	City	County	Enrollment	Founded	Age
0	Humboldt State University	Arcata	Humboldt	7,774	1913	110
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965	58
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869	154
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002	21
...	...	...	...	...	...	...
28	University of California, Santa Cruz	Santa Cruz	Santa Cruz	19,700	1965	58
29	California State University, Monterey Bay	Seaside-Marina	Monterey	7,079	1994	29
30	California State University, Stanislaus	Turlock	Stanislaus	10,214	1957	66
31	California State University Maritime Academy	Vallejo	Solano	1,017	1929	94

32 rows × 6 columns

schools.head()

	Name	City	County	Enrollment	Founded
0	Humboldt State University	Arcata	Humboldt	7,774	1913
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002
4	California State University, Dominguez Hills	Carson	Los Angeles	15,741	1960

As an aside, you should try your best to write chained pandas code, as follows:

(
    schools
    .assign(Age=(2023 - schools['Founded']))
    .assign(is_UC=schools['Name'].str.contains('University of California'))
)

	Name	City	County	Enrollment	Founded	Age	is_UC
0	Humboldt State University	Arcata	Humboldt	7,774	1913	110	False
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965	58	False
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869	154	True
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002	21	False
...	...	...	...	...	...	...	...
28	University of California, Santa Cruz	Santa Cruz	Santa Cruz	19,700	1965	58	True
29	California State University, Monterey Bay	Seaside-Marina	Monterey	7,079	1994	29	False
30	California State University, Stanislaus	Turlock	Stanislaus	10,214	1957	66	False
31	California State University Maritime Academy	Vallejo	Solano	1,017	1929	94	False

32 rows × 7 columns

schools

	Name	City	County	Enrollment	Founded
0	Humboldt State University	Arcata	Humboldt	7,774	1913
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002
...	...	...	...	...	...
28	University of California, Santa Cruz	Santa Cruz	Santa Cruz	19,700	1965
29	California State University, Monterey Bay	Seaside-Marina	Monterey	7,079	1994
30	California State University, Stanislaus	Turlock	Stanislaus	10,214	1957
31	California State University Maritime Academy	Vallejo	Solano	1,017	1929

32 rows × 5 columns

You can also use assign when the desired column name has spaces, by using keyword arguments.

(
    schools
    .assign(**{'Years since Founding': 2023 - schools['Founded']})
)

	Name	City	County	Enrollment	Founded	Years since Founding
0	Humboldt State University	Arcata	Humboldt	7,774	1913	110
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965	58
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869	154
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002	21
...	...	...	...	...	...	...
28	University of California, Santa Cruz	Santa Cruz	Santa Cruz	19,700	1965	58
29	California State University, Monterey Bay	Seaside-Marina	Monterey	7,079	1994	29
30	California State University, Stanislaus	Turlock	Stanislaus	10,214	1957	66
31	California State University Maritime Academy	Vallejo	Solano	1,017	1929	94

32 rows × 6 columns

Adding and modifying columns, in-place

• You can assign a new column to a DataFrame in-place using [].
  • This works like dictionary assignment.
  • This modifies the underlying DataFrame, unlike assign, which returns a new DataFrame.
• This is the more "common" way of adding/modifying columns.
  • ⚠️ Warning: Exercise caution when using this approach, since this approach changes the values of existing variables.

# By default, .copy() returns a deep copy of the object it is called on,
# meaning that if you change the copy the original remains unmodified.
schools_copy = schools.copy()
schools_copy.head()

	Name	City	County	Enrollment	Founded
0	Humboldt State University	Arcata	Humboldt	7,774	1913
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002
4	California State University, Dominguez Hills	Carson	Los Angeles	15,741	1960

schools_copy['Age'] = 2023 - schools_copy['Founded']

schools_copy['Name'] = schools_copy['Name'].str.replace('University of California,', 'UC')

Note that we never reassigned schools_copy in the two cells above – that is, we never wrote schools_copy = ... – though it was still modified.

schools_copy.head()

	Name	City	County	Enrollment	Founded	Age
0	Humboldt State University	Arcata	Humboldt	7,774	1913	110
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965	58
2	UC Berkeley	Berkeley	Alameda	42,519	1869	154
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002	21
4	California State University, Dominguez Hills	Carson	Los Angeles	15,741	1960	63

Mutability

DataFrames, like lists, arrays, and dictionaries, are mutable. As you learned in DSC 20, this means that they can be modified after being created.

Not only does this explain the behavior on the previous slide, but it also explains the following:

schools.head()

	Name	City	County	Enrollment	Founded
0	Humboldt State University	Arcata	Humboldt	7,774	1913
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002
4	California State University, Dominguez Hills	Carson	Los Angeles	15,741	1960

def calculate_age(df):
    df['Age'] = 2023 - df['Founded']
    return df

calculate_age(schools)

	Name	City	County	Enrollment	Founded	Age
0	Humboldt State University	Arcata	Humboldt	7,774	1913	110
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965	58
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869	154
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002	21
...	...	...	...	...	...	...
28	University of California, Santa Cruz	Santa Cruz	Santa Cruz	19,700	1965	58
29	California State University, Monterey Bay	Seaside-Marina	Monterey	7,079	1994	29
30	California State University, Stanislaus	Turlock	Stanislaus	10,214	1957	66
31	California State University Maritime Academy	Vallejo	Solano	1,017	1929	94

32 rows × 6 columns

schools.head()

	Name	City	County	Enrollment	Founded	Age
0	Humboldt State University	Arcata	Humboldt	7,774	1913	110
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965	58
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869	154
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002	21
4	California State University, Dominguez Hills	Carson	Los Angeles	15,741	1960	63

Note that schools was modified, even though we didn't reassign it! These unintended consequences can influence the behavior of test cases on labs and projects, among other things!

To avoid this, it's a good idea to include df = df.copy() as the first line in functions that take DataFrames as input.

#How to delete a column?
#schools.drop('Age', axis=1,inplace=True)

def calculate_age(df):
    df = df.copy()
    # Now, the df referenced below is a fresh copy that is unrelated to the df passed in.
    df['Age'] = 2023 - df['Founded']
    return df

calculate_age(schools)

	Name	City	County	Enrollment	Founded	Age
0	Humboldt State University	Arcata	Humboldt	7,774	1913	110
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965	58
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869	154
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002	21
...	...	...	...	...	...	...
28	University of California, Santa Cruz	Santa Cruz	Santa Cruz	19,700	1965	58
29	California State University, Monterey Bay	Seaside-Marina	Monterey	7,079	1994	29
30	California State University, Stanislaus	Turlock	Stanislaus	10,214	1957	66
31	California State University Maritime Academy	Vallejo	Solano	1,017	1929	94

32 rows × 6 columns

What about rows?

You can add and modify rows using loc and iloc. There's a function that can be to add rows, called pd.concat; we'll see it in a few lectures.

schools_copy.iloc[-1, :] = ['University of California, La Jolla', 
                            'La Jolla', 
                            'San Diego', 
                            '80', 
                            2023, 
                            0]
schools_copy.tail()

	Name	City	County	Enrollment	Founded	Age
27	UC Santa Barbara	Santa Barbara	Santa Barbara	24,346	1891	132
28	UC Santa Cruz	Santa Cruz	Santa Cruz	19,700	1965	58
29	California State University, Monterey Bay	Seaside-Marina	Monterey	7,079	1994	29
30	California State University, Stanislaus	Turlock	Stanislaus	10,214	1957	66
31	University of California, La Jolla	La Jolla	San Diego	80	2023	0

schools_copy.loc[-1, :] = ['La Jolla State University', 
                           'La Jolla', 
                           'San Diego', 
                           '10', 
                           2023, 
                           0]
schools_copy.tail()

	Name	City	County	Enrollment	Founded	Age
28	UC Santa Cruz	Santa Cruz	Santa Cruz	19,700	1965.0	58.0
29	California State University, Monterey Bay	Seaside-Marina	Monterey	7,079	1994.0	29.0
30	California State University, Stanislaus	Turlock	Stanislaus	10,214	1957.0	66.0
31	University of California, La Jolla	La Jolla	San Diego	80	2023.0	0.0
-1	La Jolla State University	La Jolla	San Diego	10	2023.0	0.0

Axes

Axes

• The rows and columns of a DataFrame are both stored as Series.
• The axis specifies the direction of a "slice" of a DataFrame.

• Axis 0 refers to the index (rows).
• Axis 1 refers to the columns.

DataFrame methods with axis

Consider the DataFrame A defined below using a dictionary.

A = pd.DataFrame({
    'A': [1, 4],
    'B': [2, 5],
    'C': [3, 6]
})
A

	A	B	C
0	1	2	3
1	4	5	6

If we specify axis=0, A.sum will "compress" along axis 0, and keep the column labels intact.

A.sum(axis=0)

A    5
B    7
C    9
dtype: int64

A.sum(0)

A    5
B    7
C    9
dtype: int64

If we specify axis=1, A.sum will "compress" along axis 1, and keep the row labels (index) intact.

A.sum(axis=1)

0     6
1    15
dtype: int64

What's the default axis?

A

	A	B	C
0	1	2	3
1	4	5	6

A.sum()

A    5
B    7
C    9
dtype: int64

DataFrame methods with axis

• In addition to sum, many other Series methods work on DataFrames.
• In such cases, the DataFrame method usually applies the Series method to every row or column.
• Many of these methods accept an axis argument; the default is usually axis=0.

schools.head()

	Name	City	County	Enrollment	Founded	Age
0	Humboldt State University	Arcata	Humboldt	7,774	1913	110
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965	58
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869	154
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002	21
4	California State University, Dominguez Hills	Carson	Los Angeles	15,741	1960	63

# The maximum element in each column.
schools.max()

Name          University of California, Santa Cruz
City                                       Vallejo
County                                        Yolo
Enrollment                                   9,201
Founded                                       2005
Age                                            166
dtype: object

# The number of unique values in each column.
schools.nunique()

Name          32
City          30
County        23
Enrollment    32
Founded       25
Age           25
dtype: int64

# Why is this meaningless?
schools[['Founded', 'Age']].mean(axis=1)

0     1011.5
1     1011.5
2     1011.5
3     1011.5
       ...  
28    1011.5
29    1011.5
30    1011.5
31    1011.5
Length: 32, dtype: float64

# describe doesn't accept an axis argument; it works on every numeric column in the DataFrame it is called on.
schools.describe()

	Founded	Age
count	32.000000	32.000000
mean	1938.625000	84.375000
std	38.954688	38.954688
min	1857.000000	18.000000
25%	1904.000000	61.750000
50%	1951.500000	71.500000
75%	1961.250000	119.000000
max	2005.000000	166.000000

Discussion Question

In words, what characteristic do all schools in the following DataFrame share?

schools[schools.nunique(axis=1) != schools.nunique(axis=1).max()]

Hint: What city is SDSU in? What county is it in?

pandas and numpy

numpy

• NumPy stands for "numerical Python". It is a commonly-used Python module that enables fast computation involving arrays and matrices.
• numpy's main object is the array. In numpy, arrays are:
  • Homogenous – all values are of the same type.
  • (Potentially) multi-dimensional.
• Computation in numpy is fast because:
  • Much of it is implemented in C.
  • numpy arrays are stored more efficiently in memory than, say, Python lists.
• This site provides a good overview of numpy arrays.

pandas is built upon numpy

• A Series in pandas is a numpy array with an index.
• A DataFrame is like a dictionary of columns, each of which is a numpy array.
• Many operations in pandas are fast because they use numpy's implementations.
• To access the array underlying a DataFrame or Series, use the to_numpy method.
  • ⚠️ Warning: to_numpy returns a view of the original object, not a copy! Read more in the course notes.
  • .values is a soon-to-be-deprecated version of .to_numpy().

arr = np.array([4, 2, 9, 15, -1])
arr

array([ 4,  2,  9, 15, -1])

ser = pd.Series(arr, index=['a', 'b', 'c', 'd', 'e'])
ser

a     4
b     2
c     9
d    15
e    -1
dtype: int64

conv = ser.to_numpy()
conv

array([ 4,  2,  9, 15, -1])

conv[2] = 100
conv

array([  4,   2, 100,  15,  -1])

Even though conv appears to be "detached" from ser, it is not:

ser

a      4
b      2
c    100
d     15
e     -1
dtype: int64

The dangers of for-loops

• for-loops are slow when processing large datasets. You will rarely write for-loops in DSC 80, and may be penalized on assignments for using them when unnecessary!
• One of the biggest benefits of numpy is that it supports vectorized operations.
  • If a and b are two arrays of the same length, then a + b is a new array of the same length containing the element-wise sum of a and b.
• To illustrate how much faster numpy arithmetic is than using a for-loop, let's compute the distances between the origin $(0, 0)$ and 1000 random points $(x, y)$ in $\mathbb{R}^2$:
  • Using a for-loop.
  • Using vectorized arithmetic, through numpy.

Aside: Generating data

• First, we need to create a DataFrame containing 1000 random points in 2D.
• np.random.random(N) returns an array containing N numbers selected uniformly at random from the interval $[0, 1)$.

N = 1000
x_arr = np.random.random(N)
y_arr = np.random.random(N)

coordinates = pd.DataFrame({'x': x_arr, 'y': y_arr})
coordinates.head()

	x	y
0	0.312567	0.994085
1	0.556943	0.720815
2	0.349826	0.991723
3	0.078273	0.859324
4	0.614192	0.111163

coordinates.plot(kind='scatter', x='x', y='y');

Next, let's define a function that takes in a DataFrame like coordinates and returns the distances between each point and the origin, using a for-loop.

def distances(df):
    hyp_list = []
    for i in df.index:
        dist = (df.loc[i, 'x'] ** 2 + df.loc[i, 'y'] ** 2) ** 0.5
        hyp_list.append(dist)
    return hyp_list

distances(coordinates)[:5]

[1.042067034403038,
 0.9109113751685901,
 1.051614128709497,
 0.8628816912331545,
 0.6241709804951936]

The %timeit magic command can repeatedly run any snippet of code and give us its average runtime.

%timeit distances(coordinates)

11.2 ms ± 82.3 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Now, using a vectorized approach:

%timeit (coordinates['x'] ** 2 + coordinates['y'] ** 2) ** 0.5

155 µs ± 1.8 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)

Note that "µs" refers to microseconds, which are one-millionth of a second, whereas "ms" refers to milliseconds, which are one-thousandth of a second.

Takeaway: Avoid for-loops whenever possible!

pandas data types

• Each Series (column) has a data type, which refers to the type of the values stored within. Access it using the dtypes attribute.
• A column's data type determines which operations can be applied to it.
• pandas tries to guess the correct data types for a given DataFrame, and is often wrong.
  • This can lead to incorrect calculations and poor memory/time performance.
• As a result, you will often need to explicitly convert between data types.

schools.head()

	Name	City	County	Enrollment	Founded	Age
0	Humboldt State University	Arcata	Humboldt	7,774	1913	110
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965	58
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869	154
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002	21
4	California State University, Dominguez Hills	Carson	Los Angeles	15,741	1960	63

schools.dtypes

Name          object
City          object
County        object
Enrollment    object
Founded        int64
Age            int64
dtype: object

schools['Founded'].dtypes

dtype('int64')

pandas data types

Pandas dtype	Python type	NumPy type	SQL type	Usage
int64	int	int_, int8,...,int64, uint8,...,uint64	INT, BIGINT	Integer numbers
float64	float	float_, float16, float32, float64	FLOAT	Floating point numbers
bool	bool	bool_	BOOL	True/False values
datetime64	NA	datetime64[ns]	DATETIME	Date and time values
timedelta[ns]	NA	NA	NA	Differences between two datetimes
category	NA	NA	ENUM	Finite list of text values
object	str	string, unicode	NA	Text
object	NA	object	NA	Mixed types

This article details how pandas stores different data types under the hood.

What do you think is happening here? 🚰

schools['Founded'] ** 7

0    -7565314117704225911
1     4171128846908943813
2    -6937220880839396315
3    -7155102599657796480
             ...         
28    4171128846908943813
29    8892165444775224960
30   -7645595510875556467
31   -4969729423693371399
Name: Founded, Length: 32, dtype: int64

Read this article for a discussion of how numpy/pandas int64 operations differ from vanilla int operations.

⚠️ Warning: numpy and pandas don't always make the same decisions!

numpy prefers homogenous data types to optimize memory and read/write speed. This leads to type coercion.

Notice that the array created below contains only strings, even though there was an int in the argument list.

np.array(['a', 1])

array(['a', '1'], dtype='<U21')

On the other hand, pandas likes correctness and ease-of-use. The Series created below is of type object, which preserves the original data types in the argument list.

pd.Series(['a', 1])

0    a
1    1
dtype: object

pd.Series(['a', 1]).values

array(['a', 1], dtype=object)

You can specify the data type of an array when initializing it by using the dtype argument.

np.array(['a', 1], dtype=object)

array(['a', 1], dtype=object)

pandas does make some trade-offs for efficiency, however. For instance, a Series consisting of both ints and floats is coerced to the float64 data type.

pd.Series([1, 1.0])

0    1.0
1    1.0
dtype: float64

Type conversion

You can change the data type of a Series using the .astype Series method.

For instance, we can change the data type of the 'Enrollment' column in schools to be int64, once we remove the commas.

schools.head()

	Name	City	County	Enrollment	Founded	Age
0	Humboldt State University	Arcata	Humboldt	7,774	1913	110
1	California State University, Bakersfield	Bakersfield	Kern	10,493	1965	58
2	University of California, Berkeley	Berkeley	Alameda	42,519	1869	154
3	California State University Channel Islands	Camarillo	Ventura	7,095	2002	21
4	California State University, Dominguez Hills	Carson	Los Angeles	15,741	1960	63

schools.dtypes

Name          object
City          object
County        object
Enrollment    object
Founded        int64
Age            int64
dtype: object

schools['Enrollment'] = schools['Enrollment'].str.replace(',', '').astype(int)
schools.head()

	Name	City	County	Enrollment	Founded	Age
0	Humboldt State University	Arcata	Humboldt	7774	1913	110
1	California State University, Bakersfield	Bakersfield	Kern	10493	1965	58
2	University of California, Berkeley	Berkeley	Alameda	42519	1869	154
3	California State University Channel Islands	Camarillo	Ventura	7095	2002	21
4	California State University, Dominguez Hills	Carson	Los Angeles	15741	1960	63

schools.dtypes

Name          object
City          object
County        object
Enrollment     int64
Founded        int64
Age            int64
dtype: object

Performance and memory management

As we just discovered,

• numpy is optimized for speed and memory consumption.
• pandas makes implementation choices that:
  • are slow and use a lot of memory, but
  • optimize for fast code development.

To demonstrate, let's create a large array in which all of the entries are non-negative numbers less than 255, meaning that they can be represented with 8 bits (i.e. as np.uint8s, where the "u" stands for "unsigned").

data = np.random.choice(np.arange(8), 10 ** 6)

When we tell pandas to use a dtype of uint8, the size of the resulting DataFrame is under a megabyte.

ser1 = pd.Series(data, dtype=np.uint8).to_frame()
ser1.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 1 columns):
 #   Column  Non-Null Count    Dtype
---  ------  --------------    -----
 0   0       1000000 non-null  uint8
dtypes: uint8(1)
memory usage: 976.7 KB

But by default, even though the numbers are only 8-bit, pandas uses the int64 dtype, and the resulting DataFrame is over 7 megabytes large.

ser2 = pd.Series(data).to_frame()
ser2.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1000000 entries, 0 to 999999
Data columns (total 1 columns):
 #   Column  Non-Null Count    Dtype
---  ------  --------------    -----
 0   0       1000000 non-null  int64
dtypes: int64(1)
memory usage: 7.6 MB

Aside: std

To compute the standard deviation of a Series, we can use:

• The std method.
• The np.std function.

Let's try both. What do you notice?

schools['Founded'].std()

38.95468831778126

np.std(schools['Founded'])

38.34119031798569

Aside: std

The two methods/functions use different degrees of freedom (ddof) by default.

• The std method in pandas uses ddof=1 by default (sometimes called the "sample" standard deviation):

$$\text{SD} = \sqrt{\frac{\sum_{i = 1}^n (x_i - \bar{x})^2}{n - 1}}$$

• The np.std method in numpy uses ddof=0 by default (sometimes called the "population" standard deviation):

$$\text{SD} = \sqrt{\frac{\sum_{i = 1}^n (x_i - \bar{x})^2}{n}}$$

Be careful!

schools['Founded'].std()

38.95468831778126

schools['Founded'].std(ddof=1)

38.95468831778126

schools['Founded'].std(ddof=0)

38.34119031798569

Extra: Data cleaning and plotly

Note: We may not get to these slides in lecture; refer to them for extra examples.

Example: Universities in California 📚

Let's return to schools. Towards the end of the last section, we fixed the data type of the 'Enrollment' column to be int64, which means we can now perform calculations with it.

schools.head()

	Name	City	County	Enrollment	Founded	Age
0	Humboldt State University	Arcata	Humboldt	7774	1913	110
1	California State University, Bakersfield	Bakersfield	Kern	10493	1965	58
2	University of California, Berkeley	Berkeley	Alameda	42519	1869	154
3	California State University Channel Islands	Camarillo	Ventura	7095	2002	21
4	California State University, Dominguez Hills	Carson	Los Angeles	15741	1960	63

schools.dtypes

Name          object
City          object
County        object
Enrollment     int64
Founded        int64
Age            int64
dtype: object

schools['Enrollment'].describe()

count       32.000000
mean     23649.781250
std      12477.750595
min       1017.000000
25%      13506.500000
50%      23812.000000
75%      34965.750000
max      45428.000000
Name: Enrollment, dtype: float64

Enrollment vs. year founded

schools.plot(kind='scatter', x='Founded', y='Enrollment', figsize=(10, 5));

plotly

plotly is a plotting library that creates interactive graphs. It's not included in your dsc80 conda environment, so you'll need to pip install it.

!pip install plotly

Requirement already satisfied: plotly in /nix/store/87mah7hf4cl4q9m9skkl8h6jd3j32s1n-python3-3.9.6-env/lib/python3.9/site-packages (5.3.1)
Requirement already satisfied: six in /nix/store/87mah7hf4cl4q9m9skkl8h6jd3j32s1n-python3-3.9.6-env/lib/python3.9/site-packages (from plotly) (1.16.0)
Requirement already satisfied: tenacity>=6.2.0 in /nix/store/87mah7hf4cl4q9m9skkl8h6jd3j32s1n-python3-3.9.6-env/lib/python3.9/site-packages (from plotly) (8.0.1)

import plotly.express as px

Enrollment vs. year founded, but interactive

px.scatter(schools, 
           x='Founded', 
           y='Enrollment', 
           hover_name='Name', 
           color=schools['Name'].str.contains('University of California')
           )

You can even create plotly plots by default by setting pandas' plotting backend to plotly:

pd.options.plotting.backend = 'plotly'

schools.plot(kind='scatter', 
             x='Founded', 
             y='Enrollment', 
             hover_name='Name')

Summary, next time

Summary, next time

• pandas relies heavily on numpy. An understanding of how data types work in both will allow you to write more efficient and bug-free code.
• Series and DataFrames share many methods (refer to the pandas documentation for more details).
• Most pandas methods return copies of Series/DataFrames. Be careful when using techniques that modify values in-place.
• Next time: groupby and data granularity.