import pandas as pd
import numpy as np
Working with Pandas
Download this notebook: Pandas.ipynb.
- What is it used for?
- How to import data from a csv-file
- Some examples working with pandas
Pandas stands for “Panel Data” or “Python Data Analysis” Pandas is used for analyzing, cleaning, exploring and manipulating data.
Not the scope of todays topic:
Let´s import some data with the mother of all datasets: titanic.csv
Download it here: titanic.csv
= pd.read_csv("_files/titanic.csv")
df_titanic df_titanic
PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
886 | 887 | 0 | 2 | Montvila, Rev. Juozas | male | 27.0 | 0 | 0 | 211536 | 13.0000 | NaN | S |
887 | 888 | 1 | 1 | Graham, Miss. Margaret Edith | female | 19.0 | 0 | 0 | 112053 | 30.0000 | B42 | S |
888 | 889 | 0 | 3 | Johnston, Miss. Catherine Helen "Carrie" | female | NaN | 1 | 2 | W./C. 6607 | 23.4500 | NaN | S |
889 | 890 | 1 | 1 | Behr, Mr. Karl Howell | male | 26.0 | 0 | 0 | 111369 | 30.0000 | C148 | C |
890 | 891 | 0 | 3 | Dooley, Mr. Patrick | male | 32.0 | 0 | 0 | 370376 | 7.7500 | NaN | Q |
891 rows × 12 columns
The dataframe “df_titanic” is build like a table: It has rows and columns
There is an index for the rows: 0…890 and also headers for each column: PassengerId, Survived, …
Tables consist of Panda Series. Wait a Minute, what are panda series?
print(df_titanic.loc[0]) #Print the first entry ("row") of the dataframe - loc will be explained a bit later.
print("______________________________________")
print(type(df_titanic.loc[0])) #Get the data type of the first entry
PassengerId 1
Survived 0
Pclass 3
Name Braund, Mr. Owen Harris
Sex male
Age 22.0
SibSp 1
Parch 0
Ticket A/5 21171
Fare 7.25
Cabin NaN
Embarked S
Name: 0, dtype: object
______________________________________
<class 'pandas.core.series.Series'>
= df_titanic.iloc[0] #Create a pandas series of the first entry (row)
panda_series print(panda_series)
print(panda_series.loc["Age"]) #Print the age of given passenger
print(panda_series.loc["PassengerId"]) #Print the passenderID
print(panda_series.loc["Cabin"]) #Print the cabin of the passenger - as it is not known/documented there is no entry and so it is "NaN"
PassengerId 1
Survived 0
Pclass 3
Name Braund, Mr. Owen Harris
Sex male
Age 22.0
SibSp 1
Parch 0
Ticket A/5 21171
Fare 7.25
Cabin NaN
Embarked S
Name: 0, dtype: object
22.0
1
nan
Series are working just like dictionaries in Python. Dicts, you know?
# What is a dict, how to create, access and work with them.
# Key/Value is the valueable key to understand them!
= {
my_first_dict "key": "value",
"can_be": "Everything",
"numbers": 3.1415,
"lists": ["What", "a", "great", "list!"],
"nlists": [1,2,3,4,5]
}
= {
my_second_dict "key": "value",
"What_the": my_first_dict
}
# Access the values, by using the key:
print(my_first_dict["can_be"])
print(my_first_dict["lists"][3])
print(my_first_dict["nlists"][3])
print(my_second_dict["What_the"]["nlists"][1])
Everything
list!
4
2
# Two ways to create a dict:
= {
option_1 "brand": "Ford",
"model": "Mustang",
"year": 1964
}
= dict(brand="Ford", model="Mustang", year=1964)
option_2
print(option_1)
print(option_2)
{'brand': 'Ford', 'model': 'Mustang', 'year': 1964}
{'brand': 'Ford', 'model': 'Mustang', 'year': 1964}
What are they used for?
I googled that for you and found:
Python dictionaries allow us to associate a value to a unique key, and then to quickly access this value. It’s a good idea to use them whenever we want to find (lookup for) a certain Python object. We can also use lists for this scope, but they are much slower than dictionaries. -https://www.programiz.com/python-programming/dictionary
In a nutshell, a dictionary can be defined as a collection of data stored in key/value pairs. Keys must be an immutable data type (such as string, integer or tuple), while values in a dictionary can be any Python data type. Duplicate keys are not allowed. If the same key is used twice in the same dictionary, then the last occurrence will override the first. Data stored in a dictionary can be modified so they are called mutable objects. They are unordered which means that the order in which we specified the items is not maintained. (They are ordered in version 3.7 onwards) As they are dynamic, they can grove or shrink when needed. - https://www.geeksforgeeks.org/python-dictionary/
Dictionaries in Python are incredibly versatile and useful for various tasks due to their key-value pair structure. Here are a few reasons why they’re handy: - Fast Lookups: Dictionaries use a hash table implementation, allowing for quick lookups. Accessing a value by its key is very efficient, even for large collections. - Mapping Relationships: They’re perfect for mapping relationships between entities. For instance, you can use a dictionary to store a person’s attributes (name, age, etc.) using keys like ‘name’, ‘age’, ‘address’, etc. - Flexibility: Dictionaries can store various data types as values—strings, integers, lists, other dictionaries, or even functions. This flexibility is powerful when you need to organize and manipulate different types of data. - Data Retrieval and Modification: You can easily retrieve, update, or delete values in a dictionary by referring to their keys, making it efficient to work with data. - Iterating Through Data: They support iteration, allowing you to loop through keys, values, or key-value pairs using methods like keys(), values(), and items(). - JSON-Like Structure: Dictionaries have a structure similar to JSON (JavaScript Object Notation), making them an excellent choice for handling data when working with APIs or storing configuration settings. -https://chat.openai.com/
Back 2 Pandas!
A Pandas Dataframe is build out of serveral Pandas Series. Let´s take a dict and turn it into a Data Frame:
= {
car "brand": ["Ford", "VW", "Tesla"],
"model": ["Mustang", "Beatle", "Model X"],
"year": [1964, 1954, 2023]
}
= pd.DataFrame(car)
car print(type(car["brand"]))
print(type(car["model"]))
print(type(car["year"]))
print("__________________________")
print(car["brand"])
print("__________________________")
print(car["model"])
print("__________________________")
print(car["year"])
print("__________________________")
car
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
<class 'pandas.core.series.Series'>
__________________________
0 Ford
1 VW
2 Tesla
Name: brand, dtype: object
__________________________
0 Mustang
1 Beatle
2 Model X
Name: model, dtype: object
__________________________
0 1964
1 1954
2 2023
Name: year, dtype: int64
__________________________
brand | model | year | |
---|---|---|---|
0 | Ford | Mustang | 1964 |
1 | VW | Beatle | 1954 |
2 | Tesla | Model X | 2023 |
Back to the Titanic:
= df_titanic["Age"]
age_series = df_titanic["Pclass"]
class_series = pd.concat([age_series, class_series], axis=1) #Connect some series to new a dataframe
new_df
print(age_series)
print(class_series)
0 22.0
1 38.0
2 26.0
3 35.0
4 35.0
...
886 27.0
887 19.0
888 NaN
889 26.0
890 32.0
Name: Age, Length: 891, dtype: float64
0 3
1 1
2 3
3 1
4 3
..
886 2
887 1
888 3
889 1
890 3
Name: Pclass, Length: 891, dtype: int64
print(new_df)
print(type(new_df))
Age Pclass
0 22.0 3
1 38.0 1
2 26.0 3
3 35.0 1
4 35.0 3
.. ... ...
886 27.0 2
887 19.0 1
888 NaN 3
889 26.0 1
890 32.0 3
[891 rows x 2 columns]
<class 'pandas.core.frame.DataFrame'>
Lets do some examples
# drop NaNs (not a number)
= df_titanic.dropna()
df_dropnan
print(print(df_titanic.shape)) #Size of original df
print(print(df_dropnan.shape)) #Size of nan-droped df
df_dropnan
(891, 12)
None
(183, 12)
None
PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
6 | 7 | 0 | 1 | McCarthy, Mr. Timothy J | male | 54.0 | 0 | 0 | 17463 | 51.8625 | E46 | S |
10 | 11 | 1 | 3 | Sandstrom, Miss. Marguerite Rut | female | 4.0 | 1 | 1 | PP 9549 | 16.7000 | G6 | S |
11 | 12 | 1 | 1 | Bonnell, Miss. Elizabeth | female | 58.0 | 0 | 0 | 113783 | 26.5500 | C103 | S |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
871 | 872 | 1 | 1 | Beckwith, Mrs. Richard Leonard (Sallie Monypeny) | female | 47.0 | 1 | 1 | 11751 | 52.5542 | D35 | S |
872 | 873 | 0 | 1 | Carlsson, Mr. Frans Olof | male | 33.0 | 0 | 0 | 695 | 5.0000 | B51 B53 B55 | S |
879 | 880 | 1 | 1 | Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) | female | 56.0 | 0 | 1 | 11767 | 83.1583 | C50 | C |
887 | 888 | 1 | 1 | Graham, Miss. Margaret Edith | female | 19.0 | 0 | 0 | 112053 | 30.0000 | B42 | S |
889 | 890 | 1 | 1 | Behr, Mr. Karl Howell | male | 26.0 | 0 | 0 | 111369 | 30.0000 | C148 | C |
183 rows × 12 columns
#That worked great, but the index is now a total mess! Lets fix that...
= df_dropnan.reset_index(drop=True)
df_dropnan_fixed #Df with fixed index df_dropnan_fixed
PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
1 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
2 | 7 | 0 | 1 | McCarthy, Mr. Timothy J | male | 54.0 | 0 | 0 | 17463 | 51.8625 | E46 | S |
3 | 11 | 1 | 3 | Sandstrom, Miss. Marguerite Rut | female | 4.0 | 1 | 1 | PP 9549 | 16.7000 | G6 | S |
4 | 12 | 1 | 1 | Bonnell, Miss. Elizabeth | female | 58.0 | 0 | 0 | 113783 | 26.5500 | C103 | S |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
178 | 872 | 1 | 1 | Beckwith, Mrs. Richard Leonard (Sallie Monypeny) | female | 47.0 | 1 | 1 | 11751 | 52.5542 | D35 | S |
179 | 873 | 0 | 1 | Carlsson, Mr. Frans Olof | male | 33.0 | 0 | 0 | 695 | 5.0000 | B51 B53 B55 | S |
180 | 880 | 1 | 1 | Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) | female | 56.0 | 0 | 1 | 11767 | 83.1583 | C50 | C |
181 | 888 | 1 | 1 | Graham, Miss. Margaret Edith | female | 19.0 | 0 | 0 | 112053 | 30.0000 | B42 | S |
182 | 890 | 1 | 1 | Behr, Mr. Karl Howell | male | 26.0 | 0 | 0 | 111369 | 30.0000 | C148 | C |
183 rows × 12 columns
Working with logical expressions
# How many passengers are older than 30 years?
"Age"].gt(30) # .gt() grater than/.lt() lower than df_titanic[
0 False
1 True
2 False
3 True
4 True
...
886 False
887 False
888 False
889 False
890 True
Name: Age, Length: 891, dtype: bool
#Count how many passengers are over the age of 30
print(df_titanic[df_titanic["Age"] > 30]["Age"].count())
print(df_titanic["Age"][df_titanic["Age"] > 30].count())
#Count how many people survived
print(df_titanic[df_titanic["Survived"] == True]["Survived"].count())
print(df_titanic["Survived"][df_titanic["Survived"] == 1].count())
305
305
342
342
#Count how many people had been in the first, second and third class
print("You can solve it like that:")
print(df_titanic[df_titanic["Pclass"] == 1 ]["Pclass"].count())
print(df_titanic[df_titanic["Pclass"] == 2 ]["Pclass"].count())
print(df_titanic[df_titanic["Pclass"] == 3 ]["Pclass"].count())
print("Or like that:")
print(np.sum(df_titanic["Pclass"] == 1))
print(np.sum(df_titanic["Pclass"] == 2))
print(np.sum(df_titanic["Pclass"] == 3))
print("Or even like that:")
print((df_titanic["Pclass"] == 1).sum())
print((df_titanic["Pclass"] == 2).sum())
print((df_titanic["Pclass"] == 3).sum())
# Count how many classes there had been on the iceberg-ship:
print(df_titanic["Pclass"].nunique()) #Ignore NaNs
print(df_titanic["Pclass"].unique())
You can solve it like that:
216
184
491
Or like that:
216
184
491
Or even like that:
216
184
491
3
[3 1 2]
How to use loc and iloc
Some greats links for you:
https://devopedia.org/pandas-dataframe-operations
https://www.geeksforgeeks.org/select-rows-columns-by-name-or-index-in-pandas-dataframe-using-loc-iloc/
# You will probably have no idea what I am doing here.. yet! You will understand just in few minutes!
= pd.read_csv("titanic.csv")
df_titanic
# You can access data in Pandas via Label or Location
= df_titanic.iloc[0:10, :] #Getting data by location aka. indexing
df_red print(df_red)
= df_red.loc[: ,["Name", "Age", "Pclass", "Fare"]] #Getting data by labels aka. names
df_red print(df_red)
= pd.read_csv("WX_GNP.csv", index_col="datetime") #Importing data and store them in a data frame
df = df.iloc[0:10, 4:8] #Store the first 10 rows and the 5th, 6th, 7th and 8th column
df_red_2
print(df_red_2)
df_red
PassengerId Survived Pclass \
0 1 0 3
1 2 1 1
2 3 1 3
3 4 1 1
4 5 0 3
5 6 0 3
6 7 0 1
7 8 0 3
8 9 1 3
9 10 1 2
Name Sex Age SibSp \
0 Braund, Mr. Owen Harris male 22.0 1
1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1
2 Heikkinen, Miss. Laina female 26.0 0
3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1
4 Allen, Mr. William Henry male 35.0 0
5 Moran, Mr. James male NaN 0
6 McCarthy, Mr. Timothy J male 54.0 0
7 Palsson, Master. Gosta Leonard male 2.0 3
8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0
9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1
Parch Ticket Fare Cabin Embarked
0 0 A/5 21171 7.2500 NaN S
1 0 PC 17599 71.2833 C85 C
2 0 STON/O2. 3101282 7.9250 NaN S
3 0 113803 53.1000 C123 S
4 0 373450 8.0500 NaN S
5 0 330877 8.4583 NaN Q
6 0 17463 51.8625 E46 S
7 1 349909 21.0750 NaN S
8 2 347742 11.1333 NaN S
9 0 237736 30.0708 NaN C
Name Age Pclass Fare
0 Braund, Mr. Owen Harris 22.0 3 7.2500
1 Cumings, Mrs. John Bradley (Florence Briggs Th... 38.0 1 71.2833
2 Heikkinen, Miss. Laina 26.0 3 7.9250
3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 35.0 1 53.1000
4 Allen, Mr. William Henry 35.0 3 8.0500
5 Moran, Mr. James NaN 3 8.4583
6 McCarthy, Mr. Timothy J 54.0 1 51.8625
7 Palsson, Master. Gosta Leonard 2.0 3 21.0750
8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 27.0 3 11.1333
9 Nasser, Mrs. Nicholas (Adele Achem) 14.0 2 30.0708
rain iswr ilwr ta
datetime
2019-09-04 06:00:00 0.000000 0.0000 256.326 6.28198
2019-09-04 17:00:00 0.000196 555.8030 288.803 12.52460
2019-09-05 17:00:00 0.000045 534.0110 287.089 14.26540
2019-09-06 17:00:00 0.000026 546.0080 292.024 14.13660
2019-09-07 17:00:00 0.000150 528.5820 289.508 14.62380
2019-09-08 17:00:00 0.013271 180.1880 338.708 10.60940
2019-09-09 17:00:00 1.189640 97.5642 334.505 6.71417
2019-09-10 17:00:00 0.298709 120.7900 329.075 4.38162
2019-09-11 17:00:00 0.382603 361.4880 287.548 7.71127
2019-09-12 17:00:00 0.129523 255.5080 339.109 9.77087
Name | Age | Pclass | Fare | |
---|---|---|---|---|
0 | Braund, Mr. Owen Harris | 22.0 | 3 | 7.2500 |
1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | 38.0 | 1 | 71.2833 |
2 | Heikkinen, Miss. Laina | 26.0 | 3 | 7.9250 |
3 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | 35.0 | 1 | 53.1000 |
4 | Allen, Mr. William Henry | 35.0 | 3 | 8.0500 |
5 | Moran, Mr. James | NaN | 3 | 8.4583 |
6 | McCarthy, Mr. Timothy J | 54.0 | 1 | 51.8625 |
7 | Palsson, Master. Gosta Leonard | 2.0 | 3 | 21.0750 |
8 | Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) | 27.0 | 3 | 11.1333 |
9 | Nasser, Mrs. Nicholas (Adele Achem) | 14.0 | 2 | 30.0708 |
The output of the first dataframe isn´t that great to read…
Try the function .to_string()!
print(df_titanic)
print(df_titanic.to_string()) #Try using the method .to_string()!
PassengerId Survived Pclass \
0 1 0 3
1 2 1 1
2 3 1 3
3 4 1 1
4 5 0 3
.. ... ... ...
886 887 0 2
887 888 1 1
888 889 0 3
889 890 1 1
890 891 0 3
Name Sex Age SibSp \
0 Braund, Mr. Owen Harris male 22.0 1
1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1
2 Heikkinen, Miss. Laina female 26.0 0
3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1
4 Allen, Mr. William Henry male 35.0 0
.. ... ... ... ...
886 Montvila, Rev. Juozas male 27.0 0
887 Graham, Miss. Margaret Edith female 19.0 0
888 Johnston, Miss. Catherine Helen "Carrie" female NaN 1
889 Behr, Mr. Karl Howell male 26.0 0
890 Dooley, Mr. Patrick male 32.0 0
Parch Ticket Fare Cabin Embarked
0 0 A/5 21171 7.2500 NaN S
1 0 PC 17599 71.2833 C85 C
2 0 STON/O2. 3101282 7.9250 NaN S
3 0 113803 53.1000 C123 S
4 0 373450 8.0500 NaN S
.. ... ... ... ... ...
886 0 211536 13.0000 NaN S
887 0 112053 30.0000 B42 S
888 2 W./C. 6607 23.4500 NaN S
889 0 111369 30.0000 C148 C
890 0 370376 7.7500 NaN Q
[891 rows x 12 columns]
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.00 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38.00 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.00 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.00 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.00 0 0 373450 8.0500 NaN S
5 6 0 3 Moran, Mr. James male NaN 0 0 330877 8.4583 NaN Q
6 7 0 1 McCarthy, Mr. Timothy J male 54.00 0 0 17463 51.8625 E46 S
7 8 0 3 Palsson, Master. Gosta Leonard male 2.00 3 1 349909 21.0750 NaN S
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.00 0 2 347742 11.1333 NaN S
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.00 1 0 237736 30.0708 NaN C
10 11 1 3 Sandstrom, Miss. Marguerite Rut female 4.00 1 1 PP 9549 16.7000 G6 S
11 12 1 1 Bonnell, Miss. Elizabeth female 58.00 0 0 113783 26.5500 C103 S
12 13 0 3 Saundercock, Mr. William Henry male 20.00 0 0 A/5. 2151 8.0500 NaN S
13 14 0 3 Andersson, Mr. Anders Johan male 39.00 1 5 347082 31.2750 NaN S
14 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14.00 0 0 350406 7.8542 NaN S
15 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55.00 0 0 248706 16.0000 NaN S
16 17 0 3 Rice, Master. Eugene male 2.00 4 1 382652 29.1250 NaN Q
17 18 1 2 Williams, Mr. Charles Eugene male NaN 0 0 244373 13.0000 NaN S
18 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) female 31.00 1 0 345763 18.0000 NaN S
19 20 1 3 Masselmani, Mrs. Fatima female NaN 0 0 2649 7.2250 NaN C
20 21 0 2 Fynney, Mr. Joseph J male 35.00 0 0 239865 26.0000 NaN S
21 22 1 2 Beesley, Mr. Lawrence male 34.00 0 0 248698 13.0000 D56 S
22 23 1 3 McGowan, Miss. Anna "Annie" female 15.00 0 0 330923 8.0292 NaN Q
23 24 1 1 Sloper, Mr. William Thompson male 28.00 0 0 113788 35.5000 A6 S
24 25 0 3 Palsson, Miss. Torborg Danira female 8.00 3 1 349909 21.0750 NaN S
25 26 1 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) female 38.00 1 5 347077 31.3875 NaN S
26 27 0 3 Emir, Mr. Farred Chehab male NaN 0 0 2631 7.2250 NaN C
27 28 0 1 Fortune, Mr. Charles Alexander male 19.00 3 2 19950 263.0000 C23 C25 C27 S
28 29 1 3 O'Dwyer, Miss. Ellen "Nellie" female NaN 0 0 330959 7.8792 NaN Q
29 30 0 3 Todoroff, Mr. Lalio male NaN 0 0 349216 7.8958 NaN S
30 31 0 1 Uruchurtu, Don. Manuel E male 40.00 0 0 PC 17601 27.7208 NaN C
31 32 1 1 Spencer, Mrs. William Augustus (Marie Eugenie) female NaN 1 0 PC 17569 146.5208 B78 C
32 33 1 3 Glynn, Miss. Mary Agatha female NaN 0 0 335677 7.7500 NaN Q
33 34 0 2 Wheadon, Mr. Edward H male 66.00 0 0 C.A. 24579 10.5000 NaN S
34 35 0 1 Meyer, Mr. Edgar Joseph male 28.00 1 0 PC 17604 82.1708 NaN C
35 36 0 1 Holverson, Mr. Alexander Oskar male 42.00 1 0 113789 52.0000 NaN S
36 37 1 3 Mamee, Mr. Hanna male NaN 0 0 2677 7.2292 NaN C
37 38 0 3 Cann, Mr. Ernest Charles male 21.00 0 0 A./5. 2152 8.0500 NaN S
38 39 0 3 Vander Planke, Miss. Augusta Maria female 18.00 2 0 345764 18.0000 NaN S
39 40 1 3 Nicola-Yarred, Miss. Jamila female 14.00 1 0 2651 11.2417 NaN C
40 41 0 3 Ahlin, Mrs. Johan (Johanna Persdotter Larsson) female 40.00 1 0 7546 9.4750 NaN S
41 42 0 2 Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott) female 27.00 1 0 11668 21.0000 NaN S
42 43 0 3 Kraeff, Mr. Theodor male NaN 0 0 349253 7.8958 NaN C
43 44 1 2 Laroche, Miss. Simonne Marie Anne Andree female 3.00 1 2 SC/Paris 2123 41.5792 NaN C
44 45 1 3 Devaney, Miss. Margaret Delia female 19.00 0 0 330958 7.8792 NaN Q
45 46 0 3 Rogers, Mr. William John male NaN 0 0 S.C./A.4. 23567 8.0500 NaN S
46 47 0 3 Lennon, Mr. Denis male NaN 1 0 370371 15.5000 NaN Q
47 48 1 3 O'Driscoll, Miss. Bridget female NaN 0 0 14311 7.7500 NaN Q
48 49 0 3 Samaan, Mr. Youssef male NaN 2 0 2662 21.6792 NaN C
49 50 0 3 Arnold-Franchi, Mrs. Josef (Josefine Franchi) female 18.00 1 0 349237 17.8000 NaN S
50 51 0 3 Panula, Master. Juha Niilo male 7.00 4 1 3101295 39.6875 NaN S
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865 866 1 2 Bystrom, Mrs. (Karolina) female 42.00 0 0 236852 13.0000 NaN S
866 867 1 2 Duran y More, Miss. Asuncion female 27.00 1 0 SC/PARIS 2149 13.8583 NaN C
867 868 0 1 Roebling, Mr. Washington Augustus II male 31.00 0 0 PC 17590 50.4958 A24 S
868 869 0 3 van Melkebeke, Mr. Philemon male NaN 0 0 345777 9.5000 NaN S
869 870 1 3 Johnson, Master. Harold Theodor male 4.00 1 1 347742 11.1333 NaN S
870 871 0 3 Balkic, Mr. Cerin male 26.00 0 0 349248 7.8958 NaN S
871 872 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.00 1 1 11751 52.5542 D35 S
872 873 0 1 Carlsson, Mr. Frans Olof male 33.00 0 0 695 5.0000 B51 B53 B55 S
873 874 0 3 Vander Cruyssen, Mr. Victor male 47.00 0 0 345765 9.0000 NaN S
874 875 1 2 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.00 1 0 P/PP 3381 24.0000 NaN C
875 876 1 3 Najib, Miss. Adele Kiamie "Jane" female 15.00 0 0 2667 7.2250 NaN C
876 877 0 3 Gustafsson, Mr. Alfred Ossian male 20.00 0 0 7534 9.8458 NaN S
877 878 0 3 Petroff, Mr. Nedelio male 19.00 0 0 349212 7.8958 NaN S
878 879 0 3 Laleff, Mr. Kristo male NaN 0 0 349217 7.8958 NaN S
879 880 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.00 0 1 11767 83.1583 C50 C
880 881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) female 25.00 0 1 230433 26.0000 NaN S
881 882 0 3 Markun, Mr. Johann male 33.00 0 0 349257 7.8958 NaN S
882 883 0 3 Dahlberg, Miss. Gerda Ulrika female 22.00 0 0 7552 10.5167 NaN S
883 884 0 2 Banfield, Mr. Frederick James male 28.00 0 0 C.A./SOTON 34068 10.5000 NaN S
884 885 0 3 Sutehall, Mr. Henry Jr male 25.00 0 0 SOTON/OQ 392076 7.0500 NaN S
885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.00 0 5 382652 29.1250 NaN Q
886 887 0 2 Montvila, Rev. Juozas male 27.00 0 0 211536 13.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female 19.00 0 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male 26.00 0 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.00 0 0 370376 7.7500 NaN Q
Back 2 .loc and iloc
# Access values of a dataframe via .loc and .iloc
# First index = row, second index = column
print(df_red)
print() #Print empty line
print(df_red.iloc[0,0]) #Print the 1st element of the 1st row
print(df_red.iloc[0,1]) #Print the 2nd element of the 1st row
print(df_red.iloc[0,2]) #Print the 3rd element of the 1st row
print(df_red.iloc[0,3]) #Print the 4th element of the 1st row
print() #Print empty line
print(df_red_2.loc["2019-09-04 06:00:00", "rain"]) #Print if it was raining at given timestamp (index)
print(df_red_2.loc["2019-09-04 17:00:00", "iswr"]) #Print the value of "iswr" at given timestamp (index)
print(df_red_2.loc["2019-09-08 17:00:00", "ta"]) #Print... you get the idea
print(df_red_2.loc["2019-09-10 17:00:00", "rain"])
Name Age Pclass Fare
0 Braund, Mr. Owen Harris 22.0 3 7.2500
1 Cumings, Mrs. John Bradley (Florence Briggs Th... 38.0 1 71.2833
2 Heikkinen, Miss. Laina 26.0 3 7.9250
3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 35.0 1 53.1000
4 Allen, Mr. William Henry 35.0 3 8.0500
5 Moran, Mr. James NaN 3 8.4583
6 McCarthy, Mr. Timothy J 54.0 1 51.8625
7 Palsson, Master. Gosta Leonard 2.0 3 21.0750
8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 27.0 3 11.1333
9 Nasser, Mrs. Nicholas (Adele Achem) 14.0 2 30.0708
Braund, Mr. Owen Harris
22.0
3
7.25
0.0
555.803
10.6094
0.298709
#As we said, use loc with "names" and iloc with "index" to... wait! What is that?!?!
#Answer: The Index are numerical!
print(df_red)
print("___________________________________________________________________________")
print(df_red.loc[0, "Name"])
print(df_red.loc[0, "Age"])
print(df_red.loc[0, "Pclass"])
print(df_red.loc[0, "Fare"])
print("___________________________________________________________________________")
Name Age Pclass Fare
0 Braund, Mr. Owen Harris 22.0 3 7.2500
1 Cumings, Mrs. John Bradley (Florence Briggs Th... 38.0 1 71.2833
2 Heikkinen, Miss. Laina 26.0 3 7.9250
3 Futrelle, Mrs. Jacques Heath (Lily May Peel) 35.0 1 53.1000
4 Allen, Mr. William Henry 35.0 3 8.0500
5 Moran, Mr. James NaN 3 8.4583
6 McCarthy, Mr. Timothy J 54.0 1 51.8625
7 Palsson, Master. Gosta Leonard 2.0 3 21.0750
8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 27.0 3 11.1333
9 Nasser, Mrs. Nicholas (Adele Achem) 14.0 2 30.0708
___________________________________________________________________________
Braund, Mr. Owen Harris
22.0
3
7.25
___________________________________________________________________________
= df_red
df_red_new_index = df_red_new_index.index.astype(str)
df_red_new_index.index
print(type(df_red_new_index.index[0])) #Now the index data type is a string, so the following call will not work!
# All zeros need to be converted to strings!
print("___________________________________________________________________________")
print(df_red_new_index.loc["0", "Name"])
print(df_red_new_index.loc[0, "Age"])
print(df_red_new_index.loc[0, "Pclass"])
print(df_red_new_index.loc[0, "Fare"])
print("___________________________________________________________________________")
<class 'str'>
___________________________________________________________________________
Braund, Mr. Owen Harris
KeyError: 0
= pd.Series(list("abcdef"), index=["x", "y", "z", 0, 1, 2])
s
print("I am using .loc[0] and get the value of >>>LABEL<<< \"0\"")
print(s.loc[0]) # value at index label 0
print("I am using .iloc[0] and get the value of >>>INDEX<<< 0")
print(s.iloc[0]) # value at index location 0
print("I am using .loc[0:2] and get the values FROM LABEL \"0\" to LABEL INCLUSIVE \"2\"")
print(s.loc[0:2]) # rows at index labels between 0 and 1 (inclusive)
print("I am using .iloc[0:2] and get the values FROM INDEX 0 to INDEX EXCLUSIVE 2")
print(s.iloc[0:2]) # rows at index location between 0 and 1 (exclusive)
I am using .loc[0] and get the value of >>>LABEL<<< "0"
d
I am using .iloc[0] and get the value of >>>INDEX<<< 0
a
I am using .loc[0:2] and get the values FROM LABEL "0" to LABEL INCLUSIVE "2"
0 d
1 e
2 f
dtype: object
I am using .iloc[0:2] and get the values FROM INDEX 0 to INDEX EXCLUSIVE 2
x a
y b
dtype: object
Some exercises for logical indexing
= {
data_a_dict_ed 'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emily'],
'Age': [25, 30, 35, 40, 27],
'City': ['New York', 'San Francisco', 'Los Angeles', 'Chicago', 'Boston']
}
= pd.DataFrame(data_a_dict_ed)
df
print(df)
Name Age City
0 Alice 25 New York
1 Bob 30 San Francisco
2 Charlie 35 Los Angeles
3 David 40 Chicago
4 Emily 27 Boston
Possible solutions worked out together
# I want to show just the cities using iloc and loc:
print("_______________________")
print("_______________________")
# I want to see the first three rows using iloc and loc:
print("_______________________")
print("_______________________")
# I want to see the first three rows without the names using iloc and loc:
print("_______________________")
print("_______________________")
# I want to see who lives in San Francisco, without knowing the Data Frame
print("_______________________")
print("_______________________")
# I want to know the names of peolpe that are 30 years or older
print("_______________________")
print("_______________________")
_______________________
_______________________
_______________________
_______________________
_______________________
_______________________
_______________________
_______________________
_______________________
_______________________
# I want to show just the cities using iloc and loc:
print("_______________________")
print(df.loc[:,"City"])
print(df.iloc[:, 2])
print("_______________________")
# I want to see the first three rows using iloc and loc:
print("_______________________")
print(df.iloc[0:3])
print(df.loc[:2])
print("_______________________")
# I want to see the first three rows without the names using iloc and loc:
print("_______________________")
print(df.iloc[:,1:3])
print(df.loc[:,"Age":"City"])
print(df.loc[:,["Age","City"]])
print(df.loc[:,df.columns != "Name"])
print("_______________________")
# I want to see who lives in San Francisco, without knowing the Data Frame
print("_______________________")
print(df[df["City"] == "San Francisco"])
print(df.loc[df["City"] == "San Francisco"])
print("_______________________")
# I want to know the names of peolpe that are 30 years or older
print("_______________________")
print(df[df["Age"] >= 30]["Name"])
print(df.loc[df["Age"] >= 30, "Name"])
print("_______________________")
_______________________
0 New York
1 San Francisco
2 Los Angeles
3 Chicago
4 Boston
Name: City, dtype: object
0 New York
1 San Francisco
2 Los Angeles
3 Chicago
4 Boston
Name: City, dtype: object
_______________________
_______________________
Name Age City
0 Alice 25 New York
1 Bob 30 San Francisco
2 Charlie 35 Los Angeles
Name Age City
0 Alice 25 New York
1 Bob 30 San Francisco
2 Charlie 35 Los Angeles
_______________________
_______________________
Age City
0 25 New York
1 30 San Francisco
2 35 Los Angeles
3 40 Chicago
4 27 Boston
Age City
0 25 New York
1 30 San Francisco
2 35 Los Angeles
3 40 Chicago
4 27 Boston
Age City
0 25 New York
1 30 San Francisco
2 35 Los Angeles
3 40 Chicago
4 27 Boston
Age City
0 25 New York
1 30 San Francisco
2 35 Los Angeles
3 40 Chicago
4 27 Boston
_______________________
_______________________
Name Age City
1 Bob 30 San Francisco
Name Age City
1 Bob 30 San Francisco
_______________________
_______________________
1 Bob
2 Charlie
3 David
Name: Name, dtype: object
1 Bob
2 Charlie
3 David
Name: Name, dtype: object
_______________________
Short introduction to pivots in Pandas https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.pivot.html
Reshaping Data in Pandas
# Restruct the subset and replace newly introduced "NaNs" with a blank space
= df_titanic[["PassengerId", "Survived", "Pclass", "Name"]]
df_titanic_subset = df_titanic_subset.pivot(index="PassengerId", columns="Pclass", values="Survived")
df_titanic_pivot = df_titanic_pivot.fillna(" ")
df_titanic_pivot # Now you can instantley see, if a passenger survived and in what ticket class he had. df_titanic_pivot
= pd.DataFrame({'groups': ['Group_1', 'Group_1', 'Group_2', 'Group_2', 'Group_3', 'Group_3'],
df 'Voted': ['A', 'B', 'C', 'A', 'B', 'C'],
'Age': [24, 22, 35, 31, 75, 66],
'Random': ['a', 'b', 'c', 'd', 'e', 'f']})
df
= df.pivot(index="groups", columns="Voted", values="Age") df_piv
" ") df_piv.fillna(