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Pandas Practice Code - Set 9 - Jupyter Notebook
localhost:8888/notebooks/Pandas Practice Code - Set 9.ipynb
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In [3]:
In [4]:
In [5]:
Out[3]:
survived pclass sex age sibsp parch fare embarked c lass who adult_male
0 0 3 male 22.0 1 0 7.2500 S Third man True
1 1 1 female 38.0 1 0 71.2833 C First woman False
2 1 3 female 26.0 0 0 7.9250 S Third woman False
3 1 1 female 35.0 1 0 53.1000 S First woman False
4 0 3 male 35.0 0 0 8.0500 S Third man True
Out[4]:
['Third', 'First', 'Second']
Categories (3, object): ['Third', 'First', 'Second']
Out[5]:
3
# Pandas Unique and Count Functions
# Here, you will see how you can get a list of unique
# values, the number of all unique values, and records per
# unique value from a column in a Pandas dataframe.
# You will be using the Titanic dataset once again.
import matplotlib.pyplot as plt
import seaborn as sns
# sets the default style for plotting
sns.set_style("darkgrid")
titanic_data = sns.load_dataset('titanic')
titanic_data.head()
# To find the number of all the unique values in a column, you
# can use the unique() function. The script below returns all the
# unique values from the class column from the Titanic dataset.
titanic_data["class"].unique()
# To get the count of unique values, you can use the nunique()
# method, as shown in the script below.
titanic_data["class"].nunique()
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In [6]:
In [7]:
Out[6]:
survived 891
pclass 891
sex 891
age 714
sibsp 891
parch 891
fare 891
embarked 889
class 891
who 891
adult_male 891
deck 203
embark_town 889
alive 891
alone 891
dtype: int64
Out[7]:
Third 491
First 216
Second 184
Name: class, dtype: int64
# To get the count of non-null values for all the columns in your
# dataset, you may call the count() method on the Pandas
# dataframe. The following script prints the count of the total
# number of non-null values in all the columns of the Titanic
# dataset.
titanic_data.count()
# Finally, if you want to find the number of records for all the
# unique values in a dataframe column, you may use the
# value_counts() function.
# The script below returns counts of records for all the unique
# values in the class column.
titanic_data["class"].value_counts()
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Pandas Practice Code - Set 9 - Jupyter Notebook
localhost:8888/notebooks/Pandas Practice Code - Set 9.ipynb
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In [8]:
In [9]:
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Out[8]:
pandas.core.groupby.generic.DataFrameGroupBy
Out[9]:
3
Out[10]:
class
First 216
Second 184
Third 491
dtype: int64
# Grouping Data with GroupBy
# To group data by a column value, you can use the groupby()
# function of the Pandas dataframe. You need to pass the
# column as the parameter value to the groupby() function.
# The script below groups the data in the Titanic dataset by the
# class column. Next, the type of object returned by the
# groupby() function is also printed.
titanic_gbclass = titanic_data.groupby("class")
type(titanic_gbclass)
# The above output shows that the groupby() function returns
# the DataFrameGroupBy object. You can use various attributes
# and functions of this object to get various information about
# different groups.
# For instance, to see the number of groups, you can use the
# ngroups attribute, which returns the number of groups
# (unique values). Since the number of groups in the class
# columns is 3, you will see 3 printed in the output of the script.
titanic_gbclass.ngroups
# You can use the size() function to get the number of records
# in each group.
titanic_gbclass.size()
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In [11]:
In [12]:
Out[11]:
Int64Index([ 1, 3, 6, 11, 23, 27, 30, 31, 34, 35,
...
853, 856, 857, 862, 867, 871, 872, 879, 887, 889],
dtype='int64', length=216)
Out[12]:
survived pclass s ex age sibsp parch fare embarked who adult_male deck e
class
First 1 1 male 26.0 0 0 30.00 C man True C
Second 0 2 male 27.0 0 0 13.00 S man True E
Third 0 3 male 32.0 0 0 7.75 Q man True E
# Finally, you can also get the row indexes for records in a
# particular group. The script below returns the row indexes for
# rows where the class column contains “First.”
titanic_gbclass.groups["First"]
# You can also get the first or last record from each group using
# the first() and last() functions, respectively.
# As an example, the following script returns the last record
# from each group in the class column.
titanic_gbclass.last()
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In [13]:
In [14]:
Out[13]:
survived pclass sex age sibsp parch fare embarked who adult_male deck
9 1 2 female 14.0 1 0 30.0708 C child False NaN
15 1 2 female 55.0 0 0 16.0000 S woman False NaN
17 1 2 male NaN 0 0 13.0000 S man True NaN
20 0 2 male 35.0 0 0 26.0000 S man True NaN
21 1 2 male 34.0 0 0 13.0000 S man True D
Out[14]:
class
First 80.0
Second 70.0
Third 74.0
Name: age, dtype: float64
# You can also get a dataframe that contains records belonging
# to a subgroup using the get_group() function. The following
# script returns all records from the group named “Second”
# from the class column.
titanic_second_class = titanic_gbclass.get_group("Second")
titanic_second_class.head()
# The DataFrameByGroup object can also be used to perform
# aggregate functions. For instance, you can get the maximum
# age in all the groups in the class column using the max()
# function, as shown in the following script.
titanic_gbclass.age.max()
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localhost:8888/notebooks/Pandas Practice Code - Set 9.ipynb
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In [15]:
Out[15]:
max min count median mean
class
First 512.3292 0.0 216 60.2875 84.154687
Second 73.5000 0.0 184 14.2500 20.662183
Third 69.5500 0.0 491 8.0500 13.675550
# Similarly, you can also get information based on various
# aggregate functions bypassing the list of functions to the
# agg() method.
# For instance, the script below returns the maximum, minimum,
# median, and mean, and count of age values in different groups
# in the class column.
titanic_gbclass.fare.agg(['max', 'min', 'count', 'median', 'mean'])
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