Pandas and DataFrames

In this lesson we will be exploring data analysis using Pandas.

  • College Board talks about ideas like
    • Tools. "the ability to process data depends on users capabilities and their tools"
    • Combining Data. "combine county data sets"
    • Status on Data"determining the artist with the greatest attendance during a particular month"
    • Data poses challenge. "the need to clean data", "incomplete data"
  • From Pandas Overview -- When working with tabular data, such as data stored in spreadsheets or databases, pandas is the right tool for you. pandas will help you to explore, clean, and process your data. In pandas, a data table is called a DataFrame. - PANDAS is a library, dataframes is the structure we are using

DataFrame

  • Categories, table headers, variables are in the dark boxes/metadata (things that describe what is in the grid), gray squares/data
'''Pandas is used to gather data sets through its DataFrames implementation'''
import pandas as pd

Cleaning Data

When looking at a data set, check to see what data needs to be cleaned. Examples include:

  • Missing Data Points
  • Invalid Data
  • Inaccurate Data

Run the following code to see what needs to be cleaned

import pandas as pd
# reads the JSON file and converts it to a Pandas DataFrame
df = pd.read_json('files/grade.json')

print(df)
# What part of the data set needs to be cleaned?
# From PBL learning, what is a good time to clean data?  Hint, remember Garbage in, Garbage out?
   Student ID Year in School   GPA
0         123             12  3.57
1         246             10  4.00
2         578             12  2.78
3         469             11  3.45
4         324         Junior  4.75
5         313             20  3.33
6         145             12  2.95
7         167             10  3.90
8         235      9th Grade  3.15
9         nil              9  2.80
10        469             11  3.45
11        456             10  2.75

Extracting Info

Take a look at some features that the Pandas library has that extracts info from the dataset

DataFrame Extract Column

print(df[['GPA']])

print()

#try two columns and remove the index from print statement
print(df[['Student ID','GPA']].to_string(index=False)) # hides the index of each GPA
     GPA
0   3.57
1   4.00
2   2.78
3   3.45
4   4.75
5   3.33
6   2.95
7   3.90
8   3.15
9   2.80
10  3.45
11  2.75

Student ID  GPA
       123 3.57
       246 4.00
       578 2.78
       469 3.45
       324 4.75
       313 3.33
       145 2.95
       167 3.90
       235 3.15
       nil 2.80
       469 3.45
       456 2.75

DataFrame Sort

print(df.sort_values(by=['GPA']))

print()

#sort the values in reverse order
print(df.sort_values(by=['GPA'], ascending=False)) #documentation, looking at pandas library to see what functions/values they have, descending is not a key word 
   Student ID Year in School   GPA
11        456             10  2.75
2         578             12  2.78
9         nil              9  2.80
6         145             12  2.95
8         235      9th Grade  3.15
5         313             20  3.33
3         469             11  3.45
10        469             11  3.45
0         123             12  3.57
7         167             10  3.90
1         246             10  4.00
4         324         Junior  4.75

   Student ID Year in School   GPA
4         324         Junior  4.75
1         246             10  4.00
7         167             10  3.90
0         123             12  3.57
3         469             11  3.45
10        469             11  3.45
5         313             20  3.33
8         235      9th Grade  3.15
6         145             12  2.95
9         nil              9  2.80
2         578             12  2.78
11        456             10  2.75

DataFrame Selection or Filter

print(df[df.GPA > 3.00])
   Student ID Year in School   GPA
0         123             12  3.57
1         246             10  4.00
3         469             11  3.45
4         324         Junior  4.75
5         313             20  3.33
7         167             10  3.90
8         235      9th Grade  3.15
10        469             11  3.45

DataFrame Selection Max and Min

print(df[df.GPA == df.GPA.max()])
print()
print(df[df.GPA == df.GPA.min()])
  Student ID Year in School   GPA
4        324         Junior  4.75

   Student ID Year in School   GPA
11        456             10  2.75

Create your own DataFrame

Using Pandas allows you to create your own DataFrame in Python.

Python Dictionary to Pandas DataFrame

the data can be stored as a python dictionary. add it directly to the dataframe

import pandas as pd

#the data can be stored as a python dictionary. add it directly 
dict = {
  "calories": [420, 380, 390, 400],
  "duration": [50, 40, 45, 35]
}
#stores the data in a data frame
print("-------------Dict_to_DF------------------")
df = pd.DataFrame(dict)
print(df)

print("----------Dict_to_DF_labels--------------")

#or with the index argument, you can label rows.
df = pd.DataFrame(dict, index = ["day1", "day2", "day3", "day4"])
print(df)
-------------Dict_to_DF------------------
   calories  duration
0       420        50
1       380        40
2       390        45
3       400        35
----------Dict_to_DF_labels--------------
      calories  duration
day1       420        50
day2       380        40
day3       390        45
day4       400        35

Examine DataFrame Rows

.loc prints out single, specific data

print("-------Examine Selected Rows---------")
#use a list for multiple labels:
print(df.loc[["day1", "day3"]])

#refer to the row index:
print("--------Examine Single Row-----------")
print(df.loc["day1"]) #.loc prints out single, specific data
-------Examine Selected Rows---------
      calories  duration
day1       420        50
day3       390        45
--------Examine Single Row-----------
calories    420
duration     50
Name: day1, dtype: int64

Pandas DataFrame Information

print(df.info())
<class 'pandas.core.frame.DataFrame'>
Index: 4 entries, day1 to day4
Data columns (total 2 columns):
 #   Column    Non-Null Count  Dtype
---  ------    --------------  -----
 0   calories  4 non-null      int64
 1   duration  4 non-null      int64
dtypes: int64(2)
memory usage: 268.0+ bytes
None

Example of larger data set

Pandas can read CSV and many other types of files, run the following code to see more features with a larger data set

Head gives the top data and Tail gives the lower data

import pandas as pd

#read csv and sort 'Duration' largest to smallest
df = pd.read_csv('files/data.csv').sort_values(by=['Duration'], ascending=False)

print("--Duration Top 10---------")
print(df.head(10)) # top 10

print("--Duration Bottom 10------")
print(df.tail(10)) # bottom 10
--Duration Top 10---------
     Duration  Pulse  Maxpulse  Calories
69        300    108       143    1500.2
79        270    100       131    1729.0
109       210    137       184    1860.4
60        210    108       160    1376.0
106       180     90       120     800.3
90        180    101       127     600.1
65        180     90       130     800.4
61        160    110       137    1034.4
62        160    109       135     853.0
67        150    107       130     816.0
--Duration Bottom 10------
     Duration  Pulse  Maxpulse  Calories
68         20    106       136     110.4
100        20     95       112      77.7
89         20     83       107      50.3
135        20    136       156     189.0
94         20    150       171     127.4
95         20    151       168     229.4
139        20    141       162     222.4
64         20    110       130     131.4
112        15    124       139     124.2
93         15     80       100      50.5

APIs are a Source for Writing Programs with Data

3rd Party APIs are a great source for creating Pandas Data Frames.

  • Data can be fetched and resulting json can be placed into a Data Frame
  • Observe output, this looks very similar to a Database
'''Pandas can be used to analyze data'''
import pandas as pd
import requests

def fetch():
    '''Obtain data from an endpoint'''
    url = "https://flask.nighthawkcodingsociety.com/api/covid/"
    fetch = requests.get(url)
    json = fetch.json()

    # filter data for requirement
    df = pd.DataFrame(json['countries_stat'])  # filter endpoint for country stats
    print(df.loc[0:5, 'country_name':'deaths']) # show row 0 through 5 and columns country_name through deaths
    
fetch()
  country_name       cases     deaths
0          USA  82,649,779  1,018,316
1        India  43,057,545    522,193
2       Brazil  30,345,654    662,663
3       France  28,244,977    145,020
4      Germany  24,109,433    134,624
5           UK  21,933,206    173,352

Hacks

Early Seed award

  • Add this Blog to you own Blogging site.
  • Have all lecture files saved to your files directory before Tech Talk starts. Have data.csv open in vscode. Don't tell anyone. Show to Teacher.

AP Prep

  • Add this Blog to you own Blogging site. In the Blog add notes and observations on each code cell.
  • In blog add College Board practice problems for 2.3.

The next 4 weeks, Teachers want you to improve your understanding of data. Look at the blog and others on Unit 2. Your intention is to find some things to differentiate your individual College Board project.

  • Create or Find your own dataset. The suggestion is to use a JSON file, integrating with your PBL project would be Fambulous.
When choosing a data set, think about the following:- Does it have a good sample size?    - Is there bias in the data?
- Does the data set need to be cleaned?
- What is the purpose of the data set?
- ...
  • Continue this Blog using Pandas extract info from that dataset (ex. max, min, mean, median, mode, etc.)

Hack Helpers

Here is how Mort started on this assignment by asking ChatGPT ... Regarding Python Pandas, what are some data sets that would be good for learning Pandas?

  • There are many data sets that are suitable for learning pandas, depending on your interests and the skills you want to develop. Here are some suggestions:- Titanic Dataset: This is a classic dataset for data analysis and machine learning, and is often used as an introduction to pandas. The dataset contains information about passengers on the Titanic, including their demographics, ticket class, and survival status.

    • Iris Dataset: This is another classic dataset that is often used in machine learning and data analysis courses. The dataset contains information about iris flowers, including their sepal length, sepal width, petal length, and petal width.

    • NBA Player Stats: This dataset contains information about NBA players, including their stats for various games, their positions, and their salaries. It is a great dataset for learning how to clean and manipulate data using pandas.

    • Housing Prices Dataset: This dataset contains information about housing prices in a particular city or region, including variables such as the number of bedrooms, square footage, and price. It is a great dataset for learning how to manipulate and analyze numerical data using pandas.

    • COVID-19 Dataset: This dataset contains information about the COVID-19 pandemic, including the number of cases, deaths, and recoveries in various countries and regions. It is a great dataset for learning how to manipulate and analyze time-series data using pandas.

    • World Bank Dataset: This dataset contains information about economic indicators from various countries, such as GDP, population, and poverty rates. It is a great dataset for learning how to manipulate and analyze large datasets using pandas.

  • These are just a few examples of the many datasets that are suitable for learning pandas. The best dataset for you will depend on your interests and the specific skills you want to develop.

    Follow up question, "where can I find Titanic data set?"

  • The Titanic dataset is a popular dataset for data analysis and machine learning, and it can be found on various websites and data repositories. Here are a few places where you can find the Titanic dataset:- Kaggle: The Titanic dataset is available on Kaggle, which is a popular platform for data scientists and machine learning enthusiasts. You can download the dataset from the Kaggle website after creating an account.

    • UCI Machine Learning Repository: The Titanic dataset is also available on the UCI Machine Learning Repository, which is a collection of datasets that are commonly used for machine learning research. You can download the dataset from the UCI Machine Learning Repository website.

    • Seaborn library: If you have the Seaborn library installed in your Python environment, you can load the Titanic dataset directly from the library using the following code:

      import seaborn as sns
      titanic_data = sns.load_dataset('titanic')
      

College Board practice problems for 2.3

from IPython.display import Image, display
from pathlib import Path  # https://medium.com/@ageitgey/python-3-quick-tip-the-easy-way-to-deal-with-file-paths-on-windows-mac-and-linux-11a072b58d5f

# prepares a series of images
def image_data(path=Path("images/"), images=None):  # path of static images is defaulted
    if images is None:  # default image
        images = [
            {'source': "Google", 'label': "Extracting Information", 'file': "extractinginformation.png"}
        ]
    for image in images:
        # File to open
        image['filename'] = path / image['file']  # file with path
    return images

def image_display(images):
    for image in images:  
        display(Image(filename=image['filename']))


# Run this as standalone tester to see sample data printed in Jupyter terminal
if __name__ == "__main__":

    
    # display default images from image_data()
    default_images = image_data()
    image_display(default_images)

Titanic Data

Look at a sample of data.

Help us give information about who has died on the Titanic, look for correlations.

import seaborn as sns

# Load the titanic dataset
titanic_data = sns.load_dataset('titanic')

print("Titanic Data")


print(titanic_data.columns) # titanic data set

print(titanic_data[['survived','pclass', 'sex', 'age', 'sibsp', 'parch', 'class', 'fare', 'embark_town']]) # look at selected columns
Titanic Data
Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',
       'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town',
       'alive', 'alone'],
      dtype='object')
     survived  pclass     sex   age  sibsp  parch   class     fare  \
0           0       3    male  22.0      1      0   Third   7.2500   
1           1       1  female  38.0      1      0   First  71.2833   
2           1       3  female  26.0      0      0   Third   7.9250   
3           1       1  female  35.0      1      0   First  53.1000   
4           0       3    male  35.0      0      0   Third   8.0500   
..        ...     ...     ...   ...    ...    ...     ...      ...   
886         0       2    male  27.0      0      0  Second  13.0000   
887         1       1  female  19.0      0      0   First  30.0000   
888         0       3  female   NaN      1      2   Third  23.4500   
889         1       1    male  26.0      0      0   First  30.0000   
890         0       3    male  32.0      0      0   Third   7.7500   

     embark_town  
0    Southampton  
1      Cherbourg  
2    Southampton  
3    Southampton  
4    Southampton  
..           ...  
886  Southampton  
887  Southampton  
888  Southampton  
889    Cherbourg  
890   Queenstown  

[891 rows x 9 columns]

Use Pandas to clean the data. Most analysis, like Machine Learning or even Pandas in general like data to be in standardized format. This is called 'Training' or 'Cleaning' data.

# Preprocess the data
from sklearn.preprocessing import OneHotEncoder


td = titanic_data
td.drop(['alive', 'who', 'adult_male', 'class', 'embark_town', 'deck'], axis=1, inplace=True)
td.dropna(inplace=True)
td['sex'] = td['sex'].apply(lambda x: 1 if x == 'male' else 0)
td['alone'] = td['alone'].apply(lambda x: 1 if x == True else 0)

# Encode categorical variables
enc = OneHotEncoder(handle_unknown='ignore')
enc.fit(td[['embarked']])
onehot = enc.transform(td[['embarked']]).toarray()
cols = ['embarked_' + val for val in enc.categories_[0]]
td[cols] = pd.DataFrame(onehot)
td.drop(['embarked'], axis=1, inplace=True)
td.dropna(inplace=True)

print(td)
     survived  pclass  sex   age  sibsp  parch      fare  alone  embarked_C  \
0           0       3    1  22.0      1      0    7.2500      0         0.0   
1           1       1    0  38.0      1      0   71.2833      0         1.0   
2           1       3    0  26.0      0      0    7.9250      1         0.0   
3           1       1    0  35.0      1      0   53.1000      0         0.0   
4           0       3    1  35.0      0      0    8.0500      1         0.0   
..        ...     ...  ...   ...    ...    ...       ...    ...         ...   
705         0       2    1  39.0      0      0   26.0000      1         0.0   
706         1       2    0  45.0      0      0   13.5000      1         0.0   
707         1       1    1  42.0      0      0   26.2875      1         0.0   
708         1       1    0  22.0      0      0  151.5500      1         0.0   
710         1       1    0  24.0      0      0   49.5042      1         1.0   

     embarked_Q  embarked_S  
0           0.0         1.0  
1           0.0         0.0  
2           0.0         1.0  
3           0.0         1.0  
4           0.0         1.0  
..          ...         ...  
705         0.0         1.0  
706         0.0         1.0  
707         1.0         0.0  
708         0.0         1.0  
710         0.0         0.0  

[564 rows x 11 columns]

The result of 'Training' data is making it easier to analyze or make conclusions. In looking at the Titanic, as you clean you would probably want to make assumptions on likely chance of survival.

This would involve analyzing various factors (such as age, gender, class, etc.) that may have affected a person's chances of survival, and using that information to make predictions about whether an individual would have survived or not.

  • Data description:- Survival - Survival (0 = No; 1 = Yes). Not included in test.csv file. - Pclass - Passenger Class (1 = 1st; 2 = 2nd; 3 = 3rd)

    • Name - Name
    • Sex - Sex
    • Age - Age
    • Sibsp - Number of Siblings/Spouses Aboard
    • Parch - Number of Parents/Children Aboard
    • Ticket - Ticket Number
    • Fare - Passenger Fare
    • Cabin - Cabin
    • Embarked - Port of Embarkation (C = Cherbourg; Q = Queenstown; S = Southampton)
  • Perished Mean/Average

print(titanic_data.query("survived == 0").mean())
survived       0.000000
pclass         2.464072
sex            0.844311
age           31.073353
sibsp          0.562874
parch          0.398204
fare          24.835902
alone          0.616766
embarked_C     0.185629
embarked_Q     0.038922
embarked_S     0.775449
dtype: float64
  • Survived Mean/Average

Analysis of Titanic data

  • More females survived, 0
  • Middle aged people were more likely to survive. The younger aged of the passengers.
  • The more wealth you have the better chance you have to survive.
print(td.query("survived == 1").mean())
survived       1.000000
pclass         1.878261
sex            0.326087
age           28.481522
sibsp          0.504348
parch          0.508696
fare          50.188806
alone          0.456522
embarked_C     0.152174
embarked_Q     0.034783
embarked_S     0.813043
dtype: float64

Survived Max and Min Stats

print(td.query("survived == 1").max())
print(td.query("survived == 1").min())
survived        1.0000
pclass          3.0000
sex             1.0000
age            80.0000
sibsp           4.0000
parch           5.0000
fare          512.3292
alone           1.0000
embarked_C      1.0000
embarked_Q      1.0000
embarked_S      1.0000
dtype: float64
survived      1.00
pclass        1.00
sex           0.00
age           0.75
sibsp         0.00
parch         0.00
fare          0.00
alone         0.00
embarked_C    0.00
embarked_Q    0.00
embarked_S    0.00
dtype: float64

Machine Learning

From Tutorials Point%20is,a%20consistence%20interface%20in%20Python). Scikit-learn (Sklearn) is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning and statistical modeling including classification, regression, clustering and dimensionality reduction via a consistence interface in Python.> Description from ChatGPT. The Titanic dataset is a popular dataset for data analysis and machine learning. In the context of machine learning, accuracy refers to the percentage of correctly classified instances in a set of predictions. In this case, the testing data is a subset of the original Titanic dataset that the decision tree model has not seen during training......After training the decision tree model on the training data, we can evaluate its performance on the testing data by making predictions on the testing data and comparing them to the actual outcomes. The accuracy of the decision tree classifier on the testing data tells us how well the model generalizes to new data that it hasn't seen before......For example, if the accuracy of the decision tree classifier on the testing data is 0.8 (or 80%), this means that 80% of the predictions made by the model on the testing data were correct....Chance of survival could be done using various machine learning techniques, including decision trees, logistic regression, or support vector machines, among others.

  • Code Below prepares data for further analysis and provides an Accuracy. IMO, you would insert a new passenger and predict survival. Datasets could be used on various factors like prediction if a player will hit a Home Run, or a Stock will go up or down.
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Split arrays or matrices into random train and test subsets.
X = td.drop('survived', axis=1)
y = td['survived']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Train a decision tree classifier
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)

# Test the model
y_pred = dt.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('DecisionTreeClassifier Accuracy:', accuracy)

# Train a logistic regression model
logreg = LogisticRegression()
logreg.fit(X_train, y_train)

# Test the model
y_pred = logreg.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('LogisticRegression Accuracy:', accuracy)
DecisionTreeClassifier Accuracy: 0.7235294117647059
LogisticRegression Accuracy: 0.788235294117647
/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):
STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.

Increase the number of iterations (max_iter) or scale the data as shown in:
    https://scikit-learn.org/stable/modules/preprocessing.html
Please also refer to the documentation for alternative solver options:
    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
  n_iter_i = _check_optimize_result(

Penguin Data

I chose the penguin data set from seaborn

import seaborn as sns

# Load the titanic dataset
penguins_data = sns.load_dataset('penguins')

print("Penguins Data")


print(penguins_data.columns) # titanic data set
print(penguins_data[['species','island', 'bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g', 'sex']]) # look at selected columns
Penguins Data
Index(['species', 'island', 'bill_length_mm', 'bill_depth_mm',
       'flipper_length_mm', 'body_mass_g', 'sex'],
      dtype='object')
    species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \
0    Adelie  Torgersen            39.1           18.7              181.0   
1    Adelie  Torgersen            39.5           17.4              186.0   
2    Adelie  Torgersen            40.3           18.0              195.0   
3    Adelie  Torgersen             NaN            NaN                NaN   
4    Adelie  Torgersen            36.7           19.3              193.0   
..      ...        ...             ...            ...                ...   
339  Gentoo     Biscoe             NaN            NaN                NaN   
340  Gentoo     Biscoe            46.8           14.3              215.0   
341  Gentoo     Biscoe            50.4           15.7              222.0   
342  Gentoo     Biscoe            45.2           14.8              212.0   
343  Gentoo     Biscoe            49.9           16.1              213.0   

     body_mass_g     sex  
0         3750.0    Male  
1         3800.0  Female  
2         3250.0  Female  
3            NaN     NaN  
4         3450.0  Female  
..           ...     ...  
339          NaN     NaN  
340       4850.0  Female  
341       5750.0    Male  
342       5200.0  Female  
343       5400.0    Male  

[344 rows x 7 columns]

Penguin Data in Data Frame

from sklearn.preprocessing import OneHotEncoder


pd = penguins_data
pd.drop(['species','island', 'bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g', 'sex'], axis=1, inplace=True)
pd.dropna(inplace=True)

# Encode categorical variables
enc = OneHotEncoder(handle_unknown='ignore')
enc.fit(pd[['species']])
onehot = enc.transform(pd[['species']]).toarray()
cols = ['species_' + val for val in enc.categories_[0]]
pd[cols] = pd.DataFrame(onehot)
pd.drop(['species'], axis=1, inplace=True)
pd.dropna(inplace=True)

print(pd)
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/Users/jesa_1/vscode/andafp-1/_notebooks/2023-03-08Unit2.3ExtractinfInformationfromData.ipynb Cell 50 in <cell line: 5>()
      <a href='vscode-notebook-cell:/Users/jesa_1/vscode/andafp-1/_notebooks/2023-03-08Unit2.3ExtractinfInformationfromData.ipynb#Y100sZmlsZQ%3D%3D?line=0'>1</a> from sklearn.preprocessing import OneHotEncoder
      <a href='vscode-notebook-cell:/Users/jesa_1/vscode/andafp-1/_notebooks/2023-03-08Unit2.3ExtractinfInformationfromData.ipynb#Y100sZmlsZQ%3D%3D?line=3'>4</a> pd = penguins_data
----> <a href='vscode-notebook-cell:/Users/jesa_1/vscode/andafp-1/_notebooks/2023-03-08Unit2.3ExtractinfInformationfromData.ipynb#Y100sZmlsZQ%3D%3D?line=4'>5</a> pd.drop(['species','island', 'bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g', 'sex'], axis=1, inplace=True)
      <a href='vscode-notebook-cell:/Users/jesa_1/vscode/andafp-1/_notebooks/2023-03-08Unit2.3ExtractinfInformationfromData.ipynb#Y100sZmlsZQ%3D%3D?line=5'>6</a> pd.dropna(inplace=True)
      <a href='vscode-notebook-cell:/Users/jesa_1/vscode/andafp-1/_notebooks/2023-03-08Unit2.3ExtractinfInformationfromData.ipynb#Y100sZmlsZQ%3D%3D?line=7'>8</a> # Encode categorical variables
      <a href='vscode-notebook-cell:/Users/jesa_1/vscode/andafp-1/_notebooks/2023-03-08Unit2.3ExtractinfInformationfromData.ipynb#Y100sZmlsZQ%3D%3D?line=8'>9</a> # enc = OneHotEncoder(handle_unknown='ignore')
     <a href='vscode-notebook-cell:/Users/jesa_1/vscode/andafp-1/_notebooks/2023-03-08Unit2.3ExtractinfInformationfromData.ipynb#Y100sZmlsZQ%3D%3D?line=9'>10</a> # enc.fit(pd[['species']])
   (...)
     <a href='vscode-notebook-cell:/Users/jesa_1/vscode/andafp-1/_notebooks/2023-03-08Unit2.3ExtractinfInformationfromData.ipynb#Y100sZmlsZQ%3D%3D?line=13'>14</a> # pd.drop(['species'], axis=1, inplace=True)
     <a href='vscode-notebook-cell:/Users/jesa_1/vscode/andafp-1/_notebooks/2023-03-08Unit2.3ExtractinfInformationfromData.ipynb#Y100sZmlsZQ%3D%3D?line=14'>15</a> # pd.dropna(inplace=True)

File /opt/anaconda3/lib/python3.9/site-packages/pandas/util/_decorators.py:311, in deprecate_nonkeyword_arguments.<locals>.decorate.<locals>.wrapper(*args, **kwargs)
    305 if len(args) > num_allow_args:
    306     warnings.warn(
    307         msg.format(arguments=arguments),
    308         FutureWarning,
    309         stacklevel=stacklevel,
    310     )
--> 311 return func(*args, **kwargs)

File /opt/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py:4954, in DataFrame.drop(self, labels, axis, index, columns, level, inplace, errors)
   4806 @deprecate_nonkeyword_arguments(version=None, allowed_args=["self", "labels"])
   4807 def drop(
   4808     self,
   (...)
   4815     errors: str = "raise",
   4816 ):
   4817     """
   4818     Drop specified labels from rows or columns.
   4819 
   (...)
   4952             weight  1.0     0.8
   4953     """
-> 4954     return super().drop(
   4955         labels=labels,
   4956         axis=axis,
   4957         index=index,
   4958         columns=columns,
   4959         level=level,
   4960         inplace=inplace,
   4961         errors=errors,
   4962     )

File /opt/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py:4267, in NDFrame.drop(self, labels, axis, index, columns, level, inplace, errors)
   4265 for axis, labels in axes.items():
   4266     if labels is not None:
-> 4267         obj = obj._drop_axis(labels, axis, level=level, errors=errors)
   4269 if inplace:
   4270     self._update_inplace(obj)

File /opt/anaconda3/lib/python3.9/site-packages/pandas/core/generic.py:4311, in NDFrame._drop_axis(self, labels, axis, level, errors, consolidate, only_slice)
   4309         new_axis = axis.drop(labels, level=level, errors=errors)
   4310     else:
-> 4311         new_axis = axis.drop(labels, errors=errors)
   4312     indexer = axis.get_indexer(new_axis)
   4314 # Case for non-unique axis
   4315 else:

File /opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py:6644, in Index.drop(self, labels, errors)
   6642 if mask.any():
   6643     if errors != "ignore":
-> 6644         raise KeyError(f"{list(labels[mask])} not found in axis")
   6645     indexer = indexer[~mask]
   6646 return self.delete(indexer)

KeyError: "['species', 'island', 'bill_length_mm', 'bill_depth_mm', 'flipper_length_mm', 'body_mass_g'] not found in axis"