Data Analysis and Plotting in Python with Pandas¶

Andreas Herten, Jülich Supercomputing Centre, Forschungszentrum Jülich, 4 September 2023

My Motivation¶

  • I like Python
  • I like plotting data
  • I like sharing
  • I think Pandas is awesome and you should use it too
  • …but I'm no Python expert!

Motto: »Pandas as early as possible!«

Task Outline¶

  • Task 1
  • Task 2
  • Task 3
  • Task 4
  • Task 5
  • Task 6
  • Task 7
  • Task 7B
  • Task 8
  • Task 8B

Course Setup¶

  • 3½ hours, including break around 10:30
  • Alternating between lecture and hands-on
  • Please give status of hands-ons via 👍 as BigBlueButton status
  • TAs and me in the room can help with issues, either in public chat or in 1:1 chat
  • Please now open Jupyter Notebook of this session: https://go.fzj.de/jsc-pd
  • Give thumbs up! 👍

About Pandas¶

No description has been provided for this image

  • Python package (Python 2, Python 3)
  • For data analysis and manipulation
  • With data structures (multi-dimensional table; time series), operations
  • Name from »Panel Data« (multi-dimensional time series in economics)
  • Since 2008
  • Now at Pandas 2.0
  • https://pandas.pydata.org/
  • Install via PyPI: pip install pandas
  • Cheatsheet: https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf

Pandas Cohabitation¶

  • Pandas works great together with other established Python tools
    • Jupyter Notebooks
    • Plotting with matplotlib
    • Numerical analysis with numpy
    • Modelling with statsmodels, scikit-learn
    • Nicer plots with seaborn, altair, plotly
    • Performance enhancement with Cython, Numba, …
  • Tools building up on Pandas: cuDF (GPU-accelerated DataFrames in Rapids), pyarrow (Apache Arrow bindings in Python) …

First Steps¶

In [1]:
import pandas
In [2]:
import pandas as pd
In [3]:
pd.__version__
Out[3]:
'2.0.3'
In [4]:
%pdoc pd

Class docstring:
    pandas - a powerful data analysis and manipulation library for Python
    =====================================================================
    
    **pandas** is a Python package providing fast, flexible, and expressive data
    structures designed to make working with "relational" or "labeled" data both
    easy and intuitive. It aims to be the fundamental high-level building block for
    doing practical, **real world** data analysis in Python. Additionally, it has
    the broader goal of becoming **the most powerful and flexible open source data
    analysis / manipulation tool available in any language**. It is already well on
    its way toward this goal.
    
    Main Features
    -------------
    Here are just a few of the things that pandas does well:
    
      - Easy handling of missing data in floating point as well as non-floating
        point data.
      - Size mutability: columns can be inserted and deleted from DataFrame and
        higher dimensional objects
      - Automatic and explicit data alignment: objects can be explicitly aligned
        to a set of labels, or the user can simply ignore the labels and let
        `Series`, `DataFrame`, etc. automatically align the data for you in
        computations.
      - Powerful, flexible group by functionality to perform split-apply-combine
        operations on data sets, for both aggregating and transforming data.
      - Make it easy to convert ragged, differently-indexed data in other Python
        and NumPy data structures into DataFrame objects.
      - Intelligent label-based slicing, fancy indexing, and subsetting of large
        data sets.
      - Intuitive merging and joining data sets.
      - Flexible reshaping and pivoting of data sets.
      - Hierarchical labeling of axes (possible to have multiple labels per tick).
      - Robust IO tools for loading data from flat files (CSV and delimited),
        Excel files, databases, and saving/loading data from the ultrafast HDF5
        format.
      - Time series-specific functionality: date range generation and frequency
        conversion, moving window statistics, date shifting and lagging.

DataFrames¶

It's all about DataFrames¶

No description has been provided for this image

  • Data containers of Pandas:
    • Linear: Series
    • Multi Dimension: DataFrame
  • Series is only special (1D) case of DataFrame
  • → We use DataFrames as the more general case here

DataFrames¶

Construction¶

  • To show features of DataFrame, let's construct one and show by example!
  • Many construction possibilities
    • From lists, dictionaries, numpy objects
    • From CSV, HDF5, JSON, Excel, HTML, fixed-width files
    • From pickled Pandas data
    • From clipboard
    • From Feather, Parquet, SAS, SQL, Google BigQuery, STATA

DataFrames¶

Examples, finally¶

In [5]:
ages  = [41, 56, 56, 57, 39, 59, 43, 56, 38, 60]
In [6]:
pd.DataFrame(ages)
Out[6]:
0
0 41
1 56
2 56
3 57
4 39
5 59
6 43
7 56
8 38
9 60
In [7]:
df_ages = pd.DataFrame(ages)
df_ages.head(3)
Out[7]:
0
0 41
1 56
2 56
  • Let's add names to ages; put everything into a dict()
In [8]:
data = {
    "Name": ["Liu", "Rowland", "Rivers", "Waters", "Rice", "Fields", "Kerr", "Romero", "Davis", "Hall"],
    "Age": ages
}
print(data)

{'Name': ['Liu', 'Rowland', 'Rivers', 'Waters', 'Rice', 'Fields', 'Kerr', 'Romero', 'Davis', 'Hall'], 'Age': [41, 56, 56, 57, 39, 59, 43, 56, 38, 60]}
In [9]:
df_sample = pd.DataFrame(data)
df_sample.head(4)
Out[9]:
Name Age
0 Liu 41
1 Rowland 56
2 Rivers 56
3 Waters 57
  • Automatically creates columns from dictionary
  • Two columns now; one for names, one for ages
In [10]:
df_sample.columns
Out[10]:
Index(['Name', 'Age'], dtype='object')
  • First column is index
  • DataFrame always have indexes; auto-generated or custom
In [11]:
df_sample.index
Out[11]:
RangeIndex(start=0, stop=10, step=1)
  • Make Name be index with .set_index()
  • inplace=True will modifiy the parent frame (I don't like it)
In [12]:
df_sample.set_index("Name", inplace=True)
df_sample
Out[12]:
Age
Name
Liu 41
Rowland 56
Rivers 56
Waters 57
Rice 39
Fields 59
Kerr 43
Romero 56
Davis 38
Hall 60
  • Some more operations
In [13]:
df_sample.describe()
Out[13]:
Age
count 10.000000
mean 50.500000
std 9.009255
min 38.000000
25% 41.500000
50% 56.000000
75% 56.750000
max 60.000000
In [14]:
df_sample.info()

<class 'pandas.core.frame.DataFrame'>
Index: 10 entries, Liu to Hall
Data columns (total 1 columns):
 #   Column  Non-Null Count  Dtype
---  ------  --------------  -----
 0   Age     10 non-null     int64
dtypes: int64(1)
memory usage: 160.0+ bytes
In [15]:
df_sample.T
Out[15]:
Name Liu Rowland Rivers Waters Rice Fields Kerr Romero Davis Hall
Age 41 56 56 57 39 59 43 56 38 60
In [16]:
df_sample.T.columns
Out[16]:
Index(['Liu', 'Rowland', 'Rivers', 'Waters', 'Rice', 'Fields', 'Kerr',
       'Romero', 'Davis', 'Hall'],
      dtype='object', name='Name')
  • Also: Arithmetic operations
In [17]:
df_sample.multiply(2).head(3)
Out[17]:
Age
Name
Liu 82
Rowland 112
Rivers 112
In [18]:
df_sample.reset_index().multiply(2).head(3)
Out[18]:
Name Age
0 LiuLiu 82
1 RowlandRowland 112
2 RiversRivers 112
In [19]:
(df_sample / 2).head(3)
Out[19]:
Age
Name
Liu 20.5
Rowland 28.0
Rivers 28.0
In [20]:
(df_sample * df_sample).head(3)
Out[20]:
Age
Name
Liu 1681
Rowland 3136
Rivers 3136
In [21]:
def mysquare(number: float) -> float:
    return number*number

df_sample.apply(mysquare).head()
# or: df_sample.apply(lambda x: x*x).head()
Out[21]:
Age
Name
Liu 1681
Rowland 3136
Rivers 3136
Waters 3249
Rice 1521
In [23]:
df_sample.apply(np.square).head()
Out[23]:
Age
Name
Liu 1681
Rowland 3136
Rivers 3136
Waters 3249
Rice 1521

Logical operations allowed as well

In [24]:
df_sample > 40
Out[24]:
Age
Name
Liu True
Rowland True
Rivers True
Waters True
Rice False
Fields True
Kerr True
Romero True
Davis False
Hall True
In [25]:
df_sample.apply(mysquare).head() == df_sample.apply(lambda x: x*x).head()
Out[25]:
Age
Name
Liu True
Rowland True
Rivers True
Waters True
Rice True

Task 1¶

TASK

  • Create data frame with
    • 6 names of dinosaurs,
    • their favourite prime number,
    • and their favorite color.
  • Play around with the frame
  • Tell me when you're done with status icon in BigBlueButton: 👍
In [26]:
happy_dinos = {
    "Dinosaur Name": [],
    "Favourite Prime": [],
    "Favourite Color": []
}
#df_dinos =
In [27]:
happy_dinos = {
    "Dinosaur Name": ["Aegyptosaurus", "Tyrannosaurus", "Panoplosaurus", "Isisaurus", "Triceratops", "Velociraptor"],
    "Favourite Prime": ["4", "8", "15", "16", "23", "42"],
    "Favourite Color": ["blue", "white", "blue", "purple", "violet", "gray"]
}
df_dinos = pd.DataFrame(happy_dinos).set_index("Dinosaur Name")
df_dinos.T
Out[27]:
Dinosaur Name Aegyptosaurus Tyrannosaurus Panoplosaurus Isisaurus Triceratops Velociraptor
Favourite Prime 4 8 15 16 23 42
Favourite Color blue white blue purple violet gray

More DataFrame examples¶

In [28]:
df_demo = pd.DataFrame({
    "A": 1.2,
    "B": pd.Timestamp('20180226'),
    "C": [(-1)**i * np.sqrt(i) + np.e * (-1)**(i-1) for i in range(5)],
    "D": pd.Categorical(["This", "column", "has", "entries", "entries"]),
    "E": "Same"
})
df_demo
Out[28]:
A B C D E
0 1.2 2018-02-26 -2.718282 This Same
1 1.2 2018-02-26 1.718282 column Same
2 1.2 2018-02-26 -1.304068 has Same
3 1.2 2018-02-26 0.986231 entries Same
4 1.2 2018-02-26 -0.718282 entries Same
In [29]:
df_demo.sort_values("C")
Out[29]:
A B C D E
0 1.2 2018-02-26 -2.718282 This Same
2 1.2 2018-02-26 -1.304068 has Same
4 1.2 2018-02-26 -0.718282 entries Same
3 1.2 2018-02-26 0.986231 entries Same
1 1.2 2018-02-26 1.718282 column Same
In [30]:
df_demo.round(2).tail(2)
Out[30]:
A B C D E
3 1.2 2018-02-26 0.99 entries Same
4 1.2 2018-02-26 -0.72 entries Same
In [31]:
df_demo.round(2)[["A", "C"]].sum()
Out[31]:
A    6.00
C   -2.03
dtype: float64
In [32]:
print(df_demo.round(2).to_latex())

\begin{tabular}{lrlrll}
\toprule
 & A & B & C & D & E \\
\midrule
0 & 1.200000 & 2018-02-26 00:00:00 & -2.720000 & This & Same \\
1 & 1.200000 & 2018-02-26 00:00:00 & 1.720000 & column & Same \\
2 & 1.200000 & 2018-02-26 00:00:00 & -1.300000 & has & Same \\
3 & 1.200000 & 2018-02-26 00:00:00 & 0.990000 & entries & Same \\
4 & 1.200000 & 2018-02-26 00:00:00 & -0.720000 & entries & Same \\
\bottomrule
\end{tabular}

Reading External Data¶

(Links to documentation)

  • .read_json()
  • .read_csv()
  • .read_hdf5()
  • .read_excel()

Example:

{
    "Character": ["Sawyer", "…", "Walt"],
    "Actor": ["Josh Holloway", "…", "Malcolm David Kelley"],
    "Main Cast": [true,  "…", false]
}
In [33]:
pd.read_json("data-lost.json").set_index("Character").sort_index()
Out[33]:
Actor Main Cast
Character
Hurley Jorge Garcia True
Jack Matthew Fox True
Kate Evangeline Lilly True
Locke Terry O'Quinn True
Sawyer Josh Holloway True
Walt Malcolm David Kelley False

Task 2¶

TASK

  • Read in data-nest.csv to DataFrame; call it df
    (Data was produced with JUBE)
  • Get to know it and play a bit with it
  • Tell me when you're done with status icon in BigBlueButton: 👍
In [34]:
!head data-nest.csv

id,Nodes,Tasks/Node,Threads/Task,Runtime Program / s,Scale,Plastic,Avg. Neuron Build Time / s,Min. Edge Build Time / s,Max. Edge Build Time / s,Min. Init. Time / s,Max. Init. Time / s,Presim. Time / s,Sim. Time / s,Virt. Memory (Sum) / kB,Local Spike Counter (Sum),Average Rate (Sum),Number of Neurons,Number of Connections,Min. Delay,Max. Delay
5,1,2,4,420.42,10,true,0.29,88.12,88.18,1.14,1.20,17.26,311.52,46560664.00,825499,7.48,112500,1265738500,1.5,1.5
5,1,4,4,200.84,10,true,0.15,46.03,46.34,0.70,1.01,7.87,142.97,46903088.00,802865,7.03,112500,1265738500,1.5,1.5
5,1,2,8,202.15,10,true,0.28,47.98,48.48,0.70,1.20,7.95,142.81,47699384.00,802865,7.03,112500,1265738500,1.5,1.5
5,1,4,8,89.57,10,true,0.15,20.41,23.21,0.23,3.04,3.19,60.31,46813040.00,821491,7.23,112500,1265738500,1.5,1.5
5,2,2,4,164.16,10,true,0.20,40.03,41.09,0.52,1.58,6.08,114.88,46937216.00,802865,7.03,112500,1265738500,1.5,1.5
5,2,4,4,77.68,10,true,0.13,20.93,21.22,0.16,0.46,3.12,52.05,47362064.00,821491,7.23,112500,1265738500,1.5,1.5
5,2,2,8,79.60,10,true,0.20,21.63,21.91,0.19,0.47,2.98,53.12,46847168.00,821491,7.23,112500,1265738500,1.5,1.5
5,2,4,8,37.20,10,true,0.13,10.08,11.60,0.10,1.63,1.24,23.29,47065232.00,818198,7.33,112500,1265738500,1.5,1.5
5,3,2,4,96.51,10,true,0.15,26.54,27.41,0.36,1.22,3.33,64.28,52256880.00,813743,7.27,112500,1265738500,1.5,1.5
In [35]:
df = pd.read_csv("data-nest.csv")
df.head()
Out[35]:
id Nodes Tasks/Node Threads/Task Runtime Program / s Scale Plastic Avg. Neuron Build Time / s Min. Edge Build Time / s Max. Edge Build Time / s ... Max. Init. Time / s Presim. Time / s Sim. Time / s Virt. Memory (Sum) / kB Local Spike Counter (Sum) Average Rate (Sum) Number of Neurons Number of Connections Min. Delay Max. Delay
0 5 1 2 4 420.42 10 True 0.29 88.12 88.18 ... 1.20 17.26 311.52 46560664.0 825499 7.48 112500 1265738500 1.5 1.5
1 5 1 4 4 200.84 10 True 0.15 46.03 46.34 ... 1.01 7.87 142.97 46903088.0 802865 7.03 112500 1265738500 1.5 1.5
2 5 1 2 8 202.15 10 True 0.28 47.98 48.48 ... 1.20 7.95 142.81 47699384.0 802865 7.03 112500 1265738500 1.5 1.5
3 5 1 4 8 89.57 10 True 0.15 20.41 23.21 ... 3.04 3.19 60.31 46813040.0 821491 7.23 112500 1265738500 1.5 1.5
4 5 2 2 4 164.16 10 True 0.20 40.03 41.09 ... 1.58 6.08 114.88 46937216.0 802865 7.03 112500 1265738500 1.5 1.5

5 rows × 21 columns

Read CSV Options¶

  • See also full API documentation
  • Important parameters
    • sep: Set separator (for example : instead of ,)
    • header: Specify info about headers for columns; able to use multi-index for columns!
    • names: Alternative to header – provide your own column titles
    • usecols: Don't read whole set of columns, but only these; works with any list (range(0:20:2))…
    • skiprows: Don't read in these rows
    • na_values: What string(s) to recognize as N/A values (which will be ignored during operations on data frame)
    • parse_dates: Try to parse dates in CSV; different behaviours as to provided data structure; optionally used together with date_parser
    • compression: Treat input file as compressed file ("infer", "gzip", "zip", …)
    • decimal: Decimal point divider – for German data…
pandas.read_csv(filepath_or_buffer, *, sep=_NoDefault.no_default, delimiter=None, header='infer', names=_NoDefault.no_default, index_col=None, usecols=None, dtype=None, engine=None, converters=None, true_values=None, false_values=None, skipinitialspace=False, skiprows=None, skipfooter=0, nrows=None, na_values=None, keep_default_na=True, na_filter=True, verbose=False, skip_blank_lines=True, parse_dates=None, infer_datetime_format=_NoDefault.no_default, keep_date_col=False, date_parser=_NoDefault.no_default, date_format=None, dayfirst=False, cache_dates=True, iterator=False, chunksize=None, compression='infer', thousands=None, decimal='.', lineterminator=None, quotechar='"', quoting=0, doublequote=True, escapechar=None, comment=None, encoding=None, encoding_errors='strict', dialect=None, on_bad_lines='error', delim_whitespace=False, low_memory=True, memory_map=False, float_precision=None, storage_options=None, dtype_backend=_NoDefault.no_default)

Slicing of Data Frames¶

  • Slicing: Select a sub-range / sub-set of entire data frame
  • Pandas documentation: Detailed documentation, short documentation

Quick Slices¶

  • Use square-bracket operators to slice data frame quickly: []
    • Use column name to select column
    • Use numerical value to select row
  • Example: Select only columnn C from df_demo
In [36]:
df_demo.head(3)
Out[36]:
A B C D E
0 1.2 2018-02-26 -2.718282 This Same
1 1.2 2018-02-26 1.718282 column Same
2 1.2 2018-02-26 -1.304068 has Same
In [37]:
df_demo['C']
Out[37]:
0   -2.718282
1    1.718282
2   -1.304068
3    0.986231
4   -0.718282
Name: C, dtype: float64
  • Instead of column name in quotes and square brackets: Name of column directly
In [38]:
df_demo.C
Out[38]:
0   -2.718282
1    1.718282
2   -1.304068
3    0.986231
4   -0.718282
Name: C, dtype: float64
  • I'm not a friend, because no spaces allowed
    (And Pandas as early as possible means labelling columns well and adding spaces)
  • Select more than one column by providing list to slice operator []
  • Example: Select list of columns A and C, ['A', 'C'] from df_demo
In [39]:
my_slice = ['A', 'C']
df_demo[my_slice]
Out[39]:
A C
0 1.2 -2.718282
1 1.2 1.718282
2 1.2 -1.304068
3 1.2 0.986231
4 1.2 -0.718282
  • Use numerical values in brackets to slice along rows
  • Use ranges just like with Python lists
In [40]:
df_demo[1:3]
Out[40]:
A B C D E
1 1.2 2018-02-26 1.718282 column Same
2 1.2 2018-02-26 -1.304068 has Same
In [41]:
df_demo[1:6:2]
Out[41]:
A B C D E
1 1.2 2018-02-26 1.718282 column Same
3 1.2 2018-02-26 0.986231 entries Same
  • Attention: location might change after re-sorting!
In [42]:
df_demo[1:3]
Out[42]:
A B C D E
1 1.2 2018-02-26 1.718282 column Same
2 1.2 2018-02-26 -1.304068 has Same
In [43]:
df_demo.sort_values("C")[1:3]
Out[43]:
A B C D E
2 1.2 2018-02-26 -1.304068 has Same
4 1.2 2018-02-26 -0.718282 entries Same

Slicing of Data Frames¶

Better Slicing¶

  • .iloc[] and .loc[]: Faster slicing interfaces with more options
In [44]:
df_demo.iloc[1:3]
Out[44]:
A B C D E
1 1.2 2018-02-26 1.718282 column Same
2 1.2 2018-02-26 -1.304068 has Same
  • Also slice along columns (second argument)
In [45]:
df_demo.iloc[1:3, [0, 2]]
Out[45]:
A C
1 1.2 1.718282
2 1.2 -1.304068
  • .iloc[]: Slice by position (numerical/integer)
  • .loc[]: Slice by label (named)
  • See difference with a proper index (and not the auto-generated default index from before)
In [46]:
df_demo_indexed = df_demo.set_index("D")
df_demo_indexed
Out[46]:
A B C E
D
This 1.2 2018-02-26 -2.718282 Same
column 1.2 2018-02-26 1.718282 Same
has 1.2 2018-02-26 -1.304068 Same
entries 1.2 2018-02-26 0.986231 Same
entries 1.2 2018-02-26 -0.718282 Same
In [47]:
df_demo_indexed.loc["entries"]
Out[47]:
A B C E
D
entries 1.2 2018-02-26 0.986231 Same
entries 1.2 2018-02-26 -0.718282 Same
In [48]:
df_demo_indexed.loc[["has", "entries"], ["A", "C"]]
Out[48]:
A C
D
has 1.2 -1.304068
entries 1.2 0.986231
entries 1.2 -0.718282

Slicing of Data Frames¶

Advanced Slicing: Logical Slicing¶

  • Slice can also be array of booleans
In [49]:
df_demo[df_demo["C"] > 0]
Out[49]:
A B C D E
1 1.2 2018-02-26 1.718282 column Same
3 1.2 2018-02-26 0.986231 entries Same
In [50]:
df_demo["C"] > 0
Out[50]:
0    False
1     True
2    False
3     True
4    False
Name: C, dtype: bool
In [51]:
df_demo[(df_demo["C"] < 0) & (df_demo["D"] == "entries")]
Out[51]:
A B C D E
4 1.2 2018-02-26 -0.718282 entries Same

Adding to Existing Data Frame¶

  • Add new columns with frame["new col"] = something or .insert()
  • Combine data frames
    • Concat: Combine several data frames along an axis
    • Merge: Combine data frames on basis of common columns; database-style
    • (Join)
    • See user guide on merging
In [52]:
df_demo.head(3)
Out[52]:
A B C D E
0 1.2 2018-02-26 -2.718282 This Same
1 1.2 2018-02-26 1.718282 column Same
2 1.2 2018-02-26 -1.304068 has Same
In [53]:
df_demo["F"] = df_demo["C"] - df_demo["A"]
df_demo.head(3)
Out[53]:
A B C D E F
0 1.2 2018-02-26 -2.718282 This Same -3.918282
1 1.2 2018-02-26 1.718282 column Same 0.518282
2 1.2 2018-02-26 -1.304068 has Same -2.504068
  • .insert() allows to specify position of insertion
  • .shape gives tuple of size of data frame, vertical, horizontal
In [54]:
df_demo.insert(df_demo.shape[1] - 1, "E2", df_demo["C"] ** 2)
df_demo.head(3)
Out[54]:
A B C D E E2 F
0 1.2 2018-02-26 -2.718282 This Same 7.389056 -3.918282
1 1.2 2018-02-26 1.718282 column Same 2.952492 0.518282
2 1.2 2018-02-26 -1.304068 has Same 1.700594 -2.504068
In [55]:
df_demo.tail(3)
Out[55]:
A B C D E E2 F
2 1.2 2018-02-26 -1.304068 has Same 1.700594 -2.504068
3 1.2 2018-02-26 0.986231 entries Same 0.972652 -0.213769
4 1.2 2018-02-26 -0.718282 entries Same 0.515929 -1.918282

Combining Frames¶

  • First, create some simpler data frame to show .concat() and .merge()
In [56]:
df_1 = pd.DataFrame({"Key": ["First", "Second"], "Value": [1, 1]})
df_1
Out[56]:
Key Value
0 First 1
1 Second 1
In [57]:
df_2 = pd.DataFrame({"Key": ["First", "Second"], "Value": [2, 2]})
df_2
Out[57]:
Key Value
0 First 2
1 Second 2
  • Concatenate list of data frame vertically (axis=0)
In [58]:
pd.concat([df_1, df_2])
Out[58]:
Key Value
0 First 1
1 Second 1
0 First 2
1 Second 2
  • Same, but re-index
In [59]:
pd.concat([df_1, df_2], ignore_index=True)
Out[59]:
Key Value
0 First 1
1 Second 1
2 First 2
3 Second 2
  • Concat, but horizontally
In [60]:
pd.concat([df_1, df_2], axis=1)
Out[60]:
Key Value Key Value
0 First 1 First 2
1 Second 1 Second 2
  • Merge on common column
In [61]:
pd.merge(df_1, df_2, on="Key")
Out[61]:
Key Value_x Value_y
0 First 1 2
1 Second 1 2

.concat() can also be used to append rows to a DataFrame:

In [62]:
pd.concat(
    [
        df_demo, 
        pd.DataFrame({"A": 1.3, "B": pd.Timestamp("2018-02-27"), "C": -0.777, "D": "has it?", "E": "Same", "F": 23}, index=[0])
    ], ignore_index=True
)
Out[62]:
A B C D E E2 F
0 1.2 2018-02-26 -2.718282 This Same 7.389056 -3.918282
1 1.2 2018-02-26 1.718282 column Same 2.952492 0.518282
2 1.2 2018-02-26 -1.304068 has Same 1.700594 -2.504068
3 1.2 2018-02-26 0.986231 entries Same 0.972652 -0.213769
4 1.2 2018-02-26 -0.718282 entries Same 0.515929 -1.918282
5 1.3 2018-02-27 -0.777000 has it? Same NaN 23.000000

Task 3¶

TASK

  • Add a column to the Nest data frame form Task 2 called Threads which is the total number of threads across all nodes (i.e. the product of threads per task and tasks per node and nodes)
  • Tell me when you're done with status icon in BigBlueButton: 👍
In [63]:
df["Threads"] = df["Nodes"] * df["Tasks/Node"] * df["Threads/Task"]
df.head()
Out[63]:
id Nodes Tasks/Node Threads/Task Runtime Program / s Scale Plastic Avg. Neuron Build Time / s Min. Edge Build Time / s Max. Edge Build Time / s ... Presim. Time / s Sim. Time / s Virt. Memory (Sum) / kB Local Spike Counter (Sum) Average Rate (Sum) Number of Neurons Number of Connections Min. Delay Max. Delay Threads
0 5 1 2 4 420.42 10 True 0.29 88.12 88.18 ... 17.26 311.52 46560664.0 825499 7.48 112500 1265738500 1.5 1.5 8
1 5 1 4 4 200.84 10 True 0.15 46.03 46.34 ... 7.87 142.97 46903088.0 802865 7.03 112500 1265738500 1.5 1.5 16
2 5 1 2 8 202.15 10 True 0.28 47.98 48.48 ... 7.95 142.81 47699384.0 802865 7.03 112500 1265738500 1.5 1.5 16
3 5 1 4 8 89.57 10 True 0.15 20.41 23.21 ... 3.19 60.31 46813040.0 821491 7.23 112500 1265738500 1.5 1.5 32
4 5 2 2 4 164.16 10 True 0.20 40.03 41.09 ... 6.08 114.88 46937216.0 802865 7.03 112500 1265738500 1.5 1.5 16

5 rows × 22 columns

In [64]:
df.columns
Out[64]:
Index(['id', 'Nodes', 'Tasks/Node', 'Threads/Task', 'Runtime Program / s',
       'Scale', 'Plastic', 'Avg. Neuron Build Time / s',
       'Min. Edge Build Time / s', 'Max. Edge Build Time / s',
       'Min. Init. Time / s', 'Max. Init. Time / s', 'Presim. Time / s',
       'Sim. Time / s', 'Virt. Memory (Sum) / kB', 'Local Spike Counter (Sum)',
       'Average Rate (Sum)', 'Number of Neurons', 'Number of Connections',
       'Min. Delay', 'Max. Delay', 'Threads'],
      dtype='object')

Aside: Plotting without Pandas¶

Matplotlib 101¶

  • Matplotlib: de-facto standard for plotting in Python
  • Main interface: pyplot; provides MATLAB-like interface
  • Better: Use object-oriented API with Figure and Axis
  • Great integration into Jupyter Notebooks
  • Since v. 3: Only support for Python 3
  • → https://matplotlib.org/
In [65]:
import matplotlib.pyplot as plt
%matplotlib inline
In [66]:
x = np.linspace(0, 2*np.pi, 400)
y = np.sin(x**2)
In [67]:
fig, ax = plt.subplots()
ax.plot(x, y)
ax.set_title('Use like this')
ax.set_xlabel("Numbers");
ax.set_ylabel("$\sqrt{x}$");
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  • Plot multiple lines into one canvas
  • Call ax.plot() multiple times
In [68]:
y2 = y/np.exp(y*1.5)
In [69]:
fig, ax = plt.subplots()
ax.plot(x, y, label="y")
ax.plot(x, y2, label="y2")
ax.legend()
ax.set_title("This plot makes no sense");
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  • Matplotlib can also plot DataFrame data
  • Because DataFrame data is only array-like data with stuff on top
In [70]:
fig, ax = plt.subplots()
ax.plot(df_demo.index, df_demo["C"], label="C")
ax.legend()
ax.set_title("Nope, no sense at all");
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Task 4¶

TASK

  • Sort the Nest data frame by threads
  • Plot "Presim. Time / s" and "Sim. Time / s" of our data frame df as a function of threads
  • Use a dashed, red line for "Presim. Time / s", a blue line for "Sim. Time / s" (see API description)
  • Don't forget to label your axes and to add a legend (1st rule of plotting)
  • Tell me when you're done with status icon in BigBlueButton: 👍
In [71]:
df.sort_values(["Threads", "Nodes", "Tasks/Node", "Threads/Task"], inplace=True)  # multi-level sort
In [72]:
fig, ax = plt.subplots(figsize=(10, 3))
ax.plot(df["Threads"], df["Presim. Time / s"], linestyle="dashed", color="red", label="Presim. Time / s")
ax.plot(df["Threads"], df["Sim. Time / s"], "-b", label="Sim. Time / s")
ax.set_xlabel("Threads")
ax.set_ylabel("Time / s")
ax.legend(loc='best');
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Plotting with Pandas¶

  • Each data frame has a .plot() function (see API)
  • Plots with Matplotlib
  • Important API options:
    • kind: 'line' (default), 'bar[h]', 'hist', 'box', 'kde', 'scatter', 'hexbin'
    • subplots: Make a sub-plot for each column (good together with sharex, sharey)
    • figsize
    • grid: Add a grid to plot (use Matplotlib options)
    • style: Line style per column (accepts list or dict)
    • logx, logy, loglog: Logarithmic plots
    • xticks, yticks: Use values for ticks
    • xlim, ylim: Limits of axes
    • yerr, xerr: Add uncertainty to data points
    • stacked: Stack a bar plot
    • secondary_y: Use a secondary y axis for this plot
    • Labeling
      • title: Add title to plot (Use a list of strings if subplots=True)
      • legend: Add a legend
      • table: If true, add table of data under plot
    • **kwds: Non-parsed keyword passed to Matplotlib's plotting methods
  • Either slice and plot…
In [73]:
df_demo["C"].plot(figsize=(10, 2));
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  • … or plot and select
In [74]:
df_demo.plot(y="C", figsize=(10, 2));
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  • I prefer slicing first:
    → Allows for further operations on the sliced data frame
In [75]:
df_demo["C"].plot(kind="bar");
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  • There are pseudo-sub-functions for each of the plot kinds
  • I prefer to just call .plot(kind="smthng")
In [76]:
df_demo["C"].plot.bar();
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In [77]:
df_demo["C"].plot(kind="bar", legend=True, figsize=(12, 4), ylim=(-1, 3), title="This is a C plot");
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Task 5¶

TASK

Use the Nest data frame df to:

  1. Make threads index of the data frame (.set_index())
  2. Plot "Presim. Time / s" and "Sim. Time / s" individually
  3. Plot them onto one common canvas!
  4. Make them have the same line colors and styles as before
  5. Add a legend, add missing axes labels
  6. Tell me when you're done with status icon in BigBlueButton: 👍
In [78]:
df.set_index("Threads", inplace=True)
In [79]:
df["Presim. Time / s"].plot(figsize=(10, 3), style="--", color="red");
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In [80]:
df["Sim. Time / s"].plot(figsize=(10, 3), style="-b");
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In [81]:
df["Presim. Time / s"].plot(style="--r", figsize=(10,3));
df["Sim. Time / s"].plot(style="-b", figsize=(10,3));
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In [82]:
ax = df[["Presim. Time / s", "Sim. Time / s"]].plot(style=["--b", "-r"], figsize=(10,3));
ax.set_ylabel("Time / s");
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More Plotting with Pandas¶

Recap: Our first proper Pandas plot¶

In [83]:
df[["Presim. Time / s", "Sim. Time / s"]].plot(figsize=(10,3));
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  • That's why I think Pandas is great!
  • It has great defaults to quickly plot data; basically publication-grade already
  • Plotting functionality is very versatile
  • Before plotting, data can be massaged within data frames, if needed

More Plotting with Pandas¶

Some versatility¶

In [84]:
df_demo[["A", "C", "F"]].plot(kind="bar", stacked=True, figsize=(10,3));
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In [85]:
df_demo[df_demo["F"] < 0][["A", "C", "F"]].plot(kind="bar", stacked=True, figsize=(10,3));
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In [86]:
df_demo[df_demo["F"] < 0][["A", "C", "F"]]\
    .plot(kind="barh", subplots=True, sharex=True, title="Subplots Demo", figsize=(10, 4));
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In [87]:
df_demo.loc[df_demo["F"] < 0, ["A", "F"]]\
    .plot(
        style=["-*r", "--ob"], 
        secondary_y="A", 
        figsize=(12, 6),
        table=True
    );
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In [88]:
df_demo.loc[df_demo["F"] < 0, ["A", "F"]]\
    .plot(
        style=["-*r", "--ob"], 
        secondary_y="A", 
        figsize=(12, 6),
        yerr={
            "A": abs(df_demo[df_demo["F"] < 0]["C"]), 
            "F": 0.2
        }, 
        capsize=4,
        title="Bug: style is ignored with yerr",
        marker="P"
    );
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Combine Pandas with Matplotlib¶

  • Pandas shortcuts very handy
  • But sometimes, one needs to access underlying Matplotlib functionality
  • No problemo!
  • Option 1: Pandas always returns axis
    • Use this to manipulate the canvas
    • Get underlying figure with ax.get_figure() (for fig.savefig())
  • Option 2: Create figure and axes with Matplotlib, use when drawing
    • .plot(): Use ax option

Option 1: Pandas Returns Axis¶

In [89]:
ax = df_demo["C"].plot(figsize=(10, 4))
ax.set_title("Hello There!");
fig = ax.get_figure()
fig.suptitle("This title is super (literally)!");
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Option 2: Draw on Matplotlib Axes¶

In [90]:
fig, ax = plt.subplots(figsize=(10, 4))
df_demo["C"].plot(ax=ax)
ax.set_title("Hello There!");
fig.suptitle("This title is super (still, literally)!");
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  • We can also get fancy!
In [91]:
fig, (ax1, ax2) = plt.subplots(ncols=2, sharey=True, figsize=(12, 4))
for ax, column, color in zip([ax1, ax2], ["C", "F"], ["blue", "#b2e123"]):
    df_demo[column].plot(ax=ax, legend=True, color=color)
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Aside: Seaborn¶

  • Python package on top of Matplotlib
  • Powerful API shortcuts for plotting of statistical data
  • Manipulate color palettes
  • Works well together with Pandas
  • Also: New, well-looking defaults for Matplotlib (IMHO)
  • → https://seaborn.pydata.org/
In [92]:
import seaborn as sns
sns.set()  # set defaults
In [93]:
df_demo[["A", "C"]].plot(figsize=(10,3));
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Seaborn Color Palette Example¶

  • Documentation
In [94]:
sns.palplot(sns.color_palette())
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In [95]:
sns.palplot(sns.color_palette("hls", 10))
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In [96]:
sns.palplot(sns.color_palette("hsv", 20))
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In [97]:
sns.palplot(sns.color_palette("Paired", 10))
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In [98]:
sns.palplot(sns.color_palette("cubehelix", 8))
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In [99]:
sns.palplot(sns.color_palette("colorblind", 10))
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Seaborn Plot Examples¶

  • Most of the time, I use a regression plot from Seaborn
In [100]:
with sns.color_palette("hls", 2):
    sns.regplot(x="C", y="F", data=df_demo);
    sns.regplot(x="C", y="E2", data=df_demo);
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  • A joint plot combines two plots relating to distribution of values into one
  • Very handy for showing a fuller picture of two-dimensionally scattered variables
In [101]:
x, y = np.random.multivariate_normal([0, 0], [[1, -.5], [-.5, 1]], size=300).T
In [102]:
sns.jointplot(x=x, y=y, kind="reg");
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Task 6¶

TASK

  • To your df Nest data frame, add a column with the unaccounted time (Unaccounted Time / s), which is the difference of program runtime, average neuron build time, minimal edge build time, minimal initialization time, presimulation time, and simulation time.
    (I know this is technically not super correct, but it will do for our example.)
  • Plot a stacked bar plot of all these columns (except for program runtime) over the threads
  • Tell me when you're done with status icon in BigBlueButton: 👍
In [103]:
cols = [
    'Avg. Neuron Build Time / s', 
    'Min. Edge Build Time / s', 
    'Min. Init. Time / s', 
    'Presim. Time / s', 
    'Sim. Time / s'
]
df["Unaccounted Time / s"] = df['Runtime Program / s']
for entry in cols:
    df["Unaccounted Time / s"] = df["Unaccounted Time / s"] - df[entry]
In [104]:
df[["Runtime Program / s", "Unaccounted Time / s", *cols]].head(2)
Out[104]:
Runtime Program / s Unaccounted Time / s Avg. Neuron Build Time / s Min. Edge Build Time / s Min. Init. Time / s Presim. Time / s Sim. Time / s
Threads
8 420.42 2.09 0.29 88.12 1.14 17.26 311.52
16 202.15 2.43 0.28 47.98 0.70 7.95 142.81
In [105]:
df[["Unaccounted Time / s", *cols]].plot(kind="bar", stacked=True, figsize=(12, 4));
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  • Make it relative to the total program run time
  • Slight complication: Our threads as indexes are not unique; we need to find new unique indexes
  • Could be anythig, but we use a multi index!
In [106]:
df_multind = df.set_index(["Nodes", "Tasks/Node", "Threads/Task"])
df_multind.head()
Out[106]:
id Runtime Program / s Scale Plastic Avg. Neuron Build Time / s Min. Edge Build Time / s Max. Edge Build Time / s Min. Init. Time / s Max. Init. Time / s Presim. Time / s Sim. Time / s Virt. Memory (Sum) / kB Local Spike Counter (Sum) Average Rate (Sum) Number of Neurons Number of Connections Min. Delay Max. Delay Unaccounted Time / s
Nodes Tasks/Node Threads/Task
1 2 4 5 420.42 10 True 0.29 88.12 88.18 1.14 1.20 17.26 311.52 46560664.0 825499 7.48 112500 1265738500 1.5 1.5 2.09
8 5 202.15 10 True 0.28 47.98 48.48 0.70 1.20 7.95 142.81 47699384.0 802865 7.03 112500 1265738500 1.5 1.5 2.43
4 4 5 200.84 10 True 0.15 46.03 46.34 0.70 1.01 7.87 142.97 46903088.0 802865 7.03 112500 1265738500 1.5 1.5 3.12
2 2 4 5 164.16 10 True 0.20 40.03 41.09 0.52 1.58 6.08 114.88 46937216.0 802865 7.03 112500 1265738500 1.5 1.5 2.45
1 2 12 6 141.70 10 True 0.30 32.93 33.26 0.62 0.95 5.41 100.16 50148824.0 813743 7.27 112500 1265738500 1.5 1.5 2.28
In [107]:
df_multind[["Unaccounted Time / s", *cols]]\
    .divide(df_multind["Runtime Program / s"], axis="index")\
    .plot(kind="bar", stacked=True, figsize=(14, 6), title="Relative Time Distribution");
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Next Level: Hierarchical Data¶

  • MultiIndex only a first level
  • More powerful:
    • Grouping: .groupby() ("Split-apply-combine", API, User Guide)
    • Pivoting: .pivot_table() (API, User Guide); also .pivot() (specialized version of .pivot_table(), API)

Grouping¶

  • Group a frame by common values of column(s)
  • Use operations on this group
  • Grouped frame is not directly a new frame, but only through an applied operation
In [108]:
df.groupby("Nodes").groups
Out[108]:
{1: [8, 16, 16, 24, 32, 48], 2: [16, 32, 32, 48, 64, 96], 3: [24, 48, 48, 72, 96, 144], 4: [32, 64, 64, 96, 128, 192], 5: [40, 80, 80, 120, 160, 240], 6: [48, 96, 96, 144, 192, 288]}
In [109]:
df.groupby("Nodes").get_group(4).head(3)
Out[109]:
id Nodes Tasks/Node Threads/Task Runtime Program / s Scale Plastic Avg. Neuron Build Time / s Min. Edge Build Time / s Max. Edge Build Time / s ... Presim. Time / s Sim. Time / s Virt. Memory (Sum) / kB Local Spike Counter (Sum) Average Rate (Sum) Number of Neurons Number of Connections Min. Delay Max. Delay Unaccounted Time / s
Threads
32 5 4 2 4 66.58 10 True 0.13 18.86 19.65 ... 2.35 43.38 47361344.0 821491 7.23 112500 1265738500 1.5 1.5 1.70
64 5 4 2 8 34.09 10 True 0.14 10.60 10.83 ... 1.25 20.96 47074752.0 818198 7.33 112500 1265738500 1.5 1.5 1.03
64 5 4 4 4 32.49 10 True 0.09 9.98 10.31 ... 1.12 20.12 48081056.0 818198 7.33 112500 1265738500 1.5 1.5 1.09

3 rows × 22 columns

In [110]:
df.groupby("Nodes").mean()
Out[110]:
id Tasks/Node Threads/Task Runtime Program / s Scale Plastic Avg. Neuron Build Time / s Min. Edge Build Time / s Max. Edge Build Time / s Min. Init. Time / s ... Presim. Time / s Sim. Time / s Virt. Memory (Sum) / kB Local Spike Counter (Sum) Average Rate (Sum) Number of Neurons Number of Connections Min. Delay Max. Delay Unaccounted Time / s
Nodes
1 5.333333 3.0 8.0 185.023333 10.0 1.0 0.220000 42.040000 42.838333 0.583333 ... 7.226667 132.061667 4.806585e+07 816298.000000 7.215000 112500.0 1.265738e+09 1.5 1.5 2.891667
2 5.333333 3.0 8.0 73.601667 10.0 1.0 0.168333 19.628333 20.313333 0.191667 ... 2.725000 48.901667 4.975288e+07 818151.000000 7.210000 112500.0 1.265738e+09 1.5 1.5 1.986667
3 5.333333 3.0 8.0 43.990000 10.0 1.0 0.138333 12.810000 13.305000 0.135000 ... 1.426667 27.735000 5.511165e+07 820465.666667 7.253333 112500.0 1.265738e+09 1.5 1.5 1.745000
4 5.333333 3.0 8.0 31.225000 10.0 1.0 0.116667 9.325000 9.740000 0.088333 ... 1.066667 19.353333 5.325783e+07 819558.166667 7.288333 112500.0 1.265738e+09 1.5 1.5 1.275000
5 5.333333 3.0 8.0 24.896667 10.0 1.0 0.140000 7.468333 7.790000 0.070000 ... 0.771667 14.950000 6.075634e+07 815307.666667 7.225000 112500.0 1.265738e+09 1.5 1.5 1.496667
6 5.333333 3.0 8.0 20.215000 10.0 1.0 0.106667 6.165000 6.406667 0.051667 ... 0.630000 12.271667 6.060652e+07 815456.333333 7.201667 112500.0 1.265738e+09 1.5 1.5 0.990000

6 rows × 21 columns

Pivoting¶

  • Combine categorically-similar columns
  • Creates hierarchical index
  • Respected during plotting with Pandas!
  • A pivot table has three layers; if confused, think about the related questions
    • index: »What's on the x axis?«
    • values: »What value do I want to plot [on the y axis]?«
    • columns: »What categories do I want [to be in the legend]?«
  • All can be populated from base data frame
  • Might be aggregated, if needed
In [111]:
df_demo["H"] = [(-1)**n for n in range(5)]
In [112]:
df_pivot = df_demo.pivot_table(
    index="F",
    values="E2",
    columns="H"
)
df_pivot
Out[112]:
H -1 1
F
-3.918282 NaN 7.389056
-2.504068 NaN 1.700594
-1.918282 NaN 0.515929
-0.213769 0.972652 NaN
0.518282 2.952492 NaN
In [113]:
df_pivot.plot(figsize=(10,3));
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Task 7¶

TASK

  • Create a pivot table based on the Nest df data frame
  • Let the x axis show the number of nodes; display the values of the simulation time "Sim. Time / s" for the tasks per node and threads per task configurations
  • Please plot a bar plot
  • Tell me when you're done with status icon in BigBlueButton: 👍
In [114]:
df.pivot_table(
    index="Nodes",
    columns=["Tasks/Node", "Threads/Task"],
    values="Sim. Time / s",
).plot(kind="bar", figsize=(12, 4));
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Task 7B (like Bonus)¶

TASK

  • Same pivot table as before (that is, x with nodes, and columns for Tasks/Node and Threads/Task)
  • But now, use Sim. Time / s and Presim. Time / s as values to show
  • Show them as a stack of those two values inside the pivot table
  • Use Panda's functionality as much as possible!

Pandas 2¶

  • Pandas 2.0 was released in April 2023
  • Only limited deprecations (i.e. an upgrade is probably safe)
  • Key new feature: Apache Arrow support (via PyArrow)
  • Fine-grained installation options python3 -m pip install 'pandas[performance, excel]'
  • Get a reasonably large data source (larger would be better, though)
  • Example: Train stations as provided by Deutsche Bahn
In [115]:
data_db = 'db-bahnhoefe.csv'  # source: https://download-data.deutschebahn.com/static/datasets/stationsdaten/DBSuS-Uebersicht_Bahnhoefe-Stand2020-03.csv
In [116]:
%timeit pd.read_csv(data_db, sep=';')

8.97 ms ± 320 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [117]:
%timeit pd.read_csv(data_db, sep=';', engine='pyarrow', dtype_backend='pyarrow')

2.54 ms ± 84.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

Large Data & Mangling¶

  • Pandas can read data directly in tar form
  • Pandas can read data directly from online resource
  • Let's combine that to an advanced task!
  • It works also with the PyArrow backend (remember to download the online resource when testing; there is no cache!)

Task 8 (Super Bonus)¶

TASK

  • Create bar chart of top 10 actors (on x) and average ratings of their top movies (y) based on IMDb data (only if they play in at least two movies)
  • IMDb provides data sets at datasets.imdbws.com
  • Can directly be loaded like
pd.read_table('https://datasets.imdbws.com/dataset.tsv.gz', sep="\t", low_memory=False, na_values=["\\N","nan"])
  • Needed:
    • name.basics.tsv.gz (for names of actors and movies they are known for)
    • title.ratings.tsv.gz (for ratings of titles)
  • Strategy suggestions:
    • Use df.apply() with custom function
    • Custom function: Compute average rating and determine if this entry is eligible for plotting (this can be done at once, but does not need to be)
    • Average rating: Look up title IDs as listed in knownForTitles in titles dataframe
df_names = pd.read_table('imdb-data/name.basics.tsv.gz', sep="\t", low_memory=False, na_values=["\\N","nan"])
df_ratings = pd.read_table('https://datasets.imdbws.com/title.ratings.tsv.gz', sep="\t", low_memory=False, na_values=["\\N","nan"])
                           
df_names_i = df_names.set_index('nconst')
df_ratings_i = df_ratings.set_index('tconst')
                           
df_names_i = pd.concat(
    [
        df_names_i, 
        df_names_i.apply(lambda line: valid_and_avg_rating(line), axis=1, result_type='expand')
    ]
    , axis=1
)
df_names_i[df_names_i['toPlot'] == True].sort_values('avgRating', ascending=False).iloc[0:10].reset_index().set_index('primaryName')['avgRating'].plot(kind='bar')

def valid_and_avg_rating(row):
    rating = 0
    ntitles = 0
    _titles = row['knownForTitles']
    _professions = row['primaryProfession']
    if not isinstance(_titles, str):
        _titles = str(_titles)
    if not isinstance(_professions, str):
        _professions = str(_professions)
    titles = _titles.split(',')
    professions = _professions.split(',')
    for title in titles:
        if title in df_ratings_i.index:
            rating += df_ratings_i.loc[title]['averageRating']
            ntitles += 1
    if ntitles > 0:
        plot = False
        if ntitles > 2:
            if 'actor' in professions:
                plot = True
        return {'toPlot': plot, 'avgRating': rating / ntitles}
    else:
        return {'toPlot': False, 'avgRating': pd.NA}

Task 8B (Bonuseption)¶

TASK

All of the following are ideas for unique sub-tasks, which can be done individually

  • In addition to Task 8, restrict the top titles to those with more than 10000 votes
  • For 30 top-rated actors, plot rating vs. age
  • For 30 top-rated actors, plot rating vs. average runtime of the known-for-titles (using title.basics.tsv.gz)

Random Features Not Shown¶

This are all links:

  • df.drop()
  • df.corr()
  • df.boxplot()
  • pd.read_sql_query("SELECT * FROM purchases", con)
  • df.duplicated() and df.drop_duplicates()
  • Aliases for categorical data
  • Working with time
    • ts.tz_convert
    • pd.period_range()
    • pd.period_range().asfreq()

Conclusion¶

  • Pandas works with and on data frames, which are central
  • Slice frames to your likings
  • Plot frames
    • Together with Matplotlib, Seaborn, others
  • Pivot tables are next level greatness
  • Remember: Pandas as early as possible!
  • Thanks for being here! 😍

Feedback to a.herten@fz-juelich.de

Next slide: Further reading

Further Reading¶

  • Pandas User Guide
  • Matplotlib and LaTeX Plots
  • towardsdatascience.com:
    • Pandas DataFrame: A lightweight Intro
    • Introduction to Data Visualization in Python
    • Basic Time Series Manipulation with Pandas
    • An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning
    • Mapping with Matplotlib, Pandas, Geopandas and Basemap in Python
    • Whats new in Pandas 2