Pandas Practice Code -Set 4

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In [3]:
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Out[3]:
survived pclass sex age sibsp parch far e embarked class who adult_male deck
0 NaN 0 3 male 22.0 1 0 7.2500 S Third man True
1 NaN 1 1 female 38.0 1 0 71.2833 C First woman False
2 NaN 1 3 female 26.0 0 0 7.9250 S Third woman False
3 NaN 1 1 female 35.0 1 0 53.1000 S First woman False
4 NaN 0 3 male 35.0 0 0 8.0500 S Third man True
Out[4]:
survived pclass sex age sibsp parch far e embarked class who adult_male d
886 NaN 0 2 male 27.0 0 0 13.00 S Second man
887 NaN 1 1 female 19.0 0 0 30.00 S First woman F
888 NaN 0 3 female NaN 1 2 23.45 S Third woman F
889 NaN 1 1 male 26.0 0 0 30.00 C First man
890 NaN 0 3 male 32.0 0 0 7.75 Q Third man
# You can import data from various sources into your Pandas
# dataframe.
# A CSV file is a type of file where each line contains a single
# record, and all the columns are separated from each other via
# a comma.
# You can read CSV files using the read_csv() function of the
# Pandas dataframe, as shown below.
import pandas as pd
titanic_data = pd.read_csv("titanic.csv")
titanic_data.head()
# If you print the dataframe header, you should see that the
# header contains first five rows
import pandas as pd
titanic_data = pd.read_csv("titanic.csv")
titanic_data.tail()
# If you print the dataframe tail, you should see that the
# tail contains last five rows
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Out[5]:
survived pclass sex a ge sibsp parch fare embarke d class who a dult_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[6]:
survived pclass a ge fare
0 0 3 22.0 7.2500
1 1 1 38.0 71.2833
2 1 3 26.0 7.9250
3 1 1 35.0 53.1000
4 0 3 35.0 8.0500
# To handle missing numerical data, we can use statistical
# techniques. The use of statistical techniques or algorithms to
# replace missing values with statistically generated values is
# called imputation.
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams["figure.figsize"] = [8,6]
sns.set_style("darkgrid")
titanic_data = sns.load_dataset('titanic')
titanic_data.head()
# Let’s filter some of the numeric columns from the dataset and
# see if they contain any missing values.
titanic_data = titanic_data[["survived", "pclass", "age", "fare"]]
titanic_data.head()
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In [7]:
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Out[7]:
survived 0.000000
pclass 0.000000
age 0.198653
fare 0.000000
dtype: float64
28.0
29.69911764705882
# To find missing values from the aforementioned columns, you
# need to first call the isnull() method on the titanic_data
# dataframe, and then you need to call the mean() method, as
# shown below.
titanic_data.isnull().mean()
# The output shows that only the age column contains
# missing values. And the ratio of missing values is around 19.86
# percent.
# Let’s now find out the median and mean values for all the nonmissing
# values in the age column.
median = titanic_data.age.median()
print(median)
mean = titanic_data.age.mean()
print(mean)
# The age column has a median value of 28 and a mean value of
# 29.6991.
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In [9]:
Out[9]:
survived pclass age fare Median_Age Mean_Age
0 0 3 22.0 7.2500 22.0 22.0
1 1 1 38.0 71.2833 38.0 38.0
2 1 3 26.0 7.9250 26.0 26.0
3 1 1 35.0 53.1000 35.0 35.0
4 0 3 35.0 8.0500 35.0 35.0
5 0 3 NaN 8.4583 28.0 29.7
6 0 1 54.0 51.8625 54.0 54.0
7 0 3 2.0 21.0750 2.0 2.0
8 1 3 27.0 11.1333 27.0 27.0
9 1 2 14.0 30.0708 14.0 14.0
10 1 3 4.0 16.7000 4.0 4.0
11 1 1 58.0 26.5500 58.0 58.0
12 0 3 20.0 8.0500 20.0 20.0
13 0 3 39.0 31.2750 39.0 39.0
14 0 3 14.0 7.8542 14.0 14.0
15 1 2 55.0 16.0000 55.0 55.0
16 0 3 2.0 29.1250 2.0 2.0
17 1 2 NaN 13.0000 28.0 29.7
18 0 3 31.0 18.0000 31.0 31.0
19 1 3 NaN 7.2250 28.0 29.7
# To plot the kernel density plots for the actual age and median
# and mean age, we will add columns to the Pandas dataframe.
import numpy as np
titanic_data['Median_Age'] = titanic_data.age.fillna(median)
titanic_data['Mean_Age'] = titanic_data.age.fillna(mean)
titanic_data['Mean_Age'] = np.round(titanic_data['Mean_Age'], 1)
titanic_data.head(20)
# The above script adds Median_Age and Mean_Age columns
# to the titanic_data dataframe and prints the first 20 records.
# Here is the output of the above script:
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<matplotlib.legend.Legend at 0x6121ef3cd0>
# Some rows in the above output show that NaN, i.e.,
# null values in the age column, have been replaced by the
# median values in the Median_Age column and by mean values
# in the Mean_Age column.
# The mean and median imputation can affect the data
# distribution for the columns containing the missing values.
# Specifically, the variance of the column is decreased by mean
# and median imputation now since more values are added to
# the center of the distribution. The following script plots the
# distribution of data for the age, Median_Age, and Mean_Age
# columns.
fig = plt.figure()
ax = fig.add_subplot(111)
titanic_data['age'] .plot(kind='kde', ax=ax)
titanic_data['Median_Age'] .plot(kind='kde', ax=ax, color='red')
titanic_data['Mean_Age'] .plot(kind='kde', ax=ax, color='green')
lines, labels = ax.get_legend_handles_labels()
ax.legend(lines, labels, loc='best')
# Here is the output of the script above:
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Out[13]:
embark_town 0.002245
age 0.198653
fare 0.000000
dtype: float64
# You can see that the default values in the age columns have
# been distorted by the mean and median imputation, and the
# overall variance of the dataset has also been decreased.
#Recommendation
# Mean and Median imputation could be used for the missing
# numerical data in case the data is missing at random. If the
# data is normally distributed, mean imputation is better, or else,
# median imputation is preferred in case of skewed
# distributions.
# One of the most common ways of handling missing values in a
# categorical column is to replace the missing values with the
# most frequently occurring values, i.e., the mode of the column.
# It is for this reason, frequent category imputation is also
# known as mode imputation. Let’s see a real-world example of
# the frequent category imputation.
# We will again use the Titanic dataset. We will first try to find
# the percentage of missing values in the age, fare, and
# embarked_ town columns.
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams["figure.figsize"] = [8,6]
sns.set_style("darkgrid")
titanic_data = sns.load_dataset('titanic')
titanic_data = titanic_data[["embark_town", "age", "fare"]]
titanic_data.head()
titanic_data.isnull().mean()
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Out[14]:
Text(0, 0.5, 'Number of Passengers')
Out[15]:
0 Southampton
dtype: object
# The output shows that embark_town and age columns have
# missing values. The ratio of missing values for the embark_
# town column is very less.
# Let’s plot the bar plot that shows each category in the
# embark_town column against the number of passengers.
titanic_data.embark_town.value_counts().sort_values(ascending=False).plot.bar()
plt.xlabel('Embark Town')
plt.ylabel('Number of Passengers')
# The output below clearly shows that most of the passengers
# embarked from Southampton.
# Let’s make sure if Southampton is the mode value for the
# embark_town column.
titanic_data.embark_town.mode()
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Out[17]:
0 24.0
dtype: float64
# Next, we can simply replace the missing values in the embark
# town column by Southampton.
titanic_data.embark_town.fillna('Southampton', inplace=True)
# Let’s now find the mode of the age column and use it to
# replace the missing values in the age column.
titanic_data.age.mode()
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In [18]:
Out[18]:
embark_town age fare age_mode
0 Southampton 22.0 7.2500 22.0
1 Cherbourg 38.0 71.2833 38.0
2 Southampton 26.0 7.9250 26.0
3 Southampton 35.0 53.1000 35.0
4 Southampton 35.0 8.0500 35.0
5 Queenstown NaN 8.4583 24.0
6 Southampton 54.0 51.8625 54.0
7 Southampton 2.0 21.0750 2.0
8 Southampton 27.0 11.1333 27.0
9 Cherbourg 14.0 30.0708 14.0
10 Southampton 4.0 16.7000 4.0
11 Southampton 58.0 26.5500 58.0
12 Southampton 20.0 8.0500 20.0
13 Southampton 39.0 31.2750 39.0
14 Southampton 14.0 7.8542 14.0
15 Southampton 55.0 16.0000 55.0
16 Queenstown 2.0 29.1250 2.0
17 Southampton NaN 13.0000 24.0
18 Southampton 31.0 18.0000 31.0
19 Cherbourg NaN 7.2250 24.0
# The output shows that the mode of the age column is 24.
# Therefore, we can use this value to replace the missing values
# in the age column.
import numpy as np
titanic_data['age_mode'] = titanic_data.age.fillna(24)
titanic_data.head(20)
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<matplotlib.legend.Legend at 0x610d9cc040>
# Finally, let’s plot the kernel density estimation plot for the
# original age column and the age column that contains the
# mode of the values in place of the missing values.
plt.rcParams["figure.figsize"] = [8,6]
fig = plt.figure()
ax = fig.add_subplot(111)
titanic_data['age'] .plot(kind='kde', ax=ax)
titanic_data['age_mode'] .plot(kind='kde', ax=ax, color='red')
lines, labels = ax.get_legend_handles_labels()
ax.legend(lines, labels, loc='best')
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In [20]:
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Out[20]:
embark_town 0.002245
age 0.198653
fare 0.000000
dtype: float64
# Missing value imputation adds an arbitrary category, e.g.,
# missing in place of the missing values. Take a look at an
# example of missing value imputation.
# Let’s load the Titanic dataset and see if any categorical
# column contains missing values.
import matplotlib.pyplot as plt
import seaborn as sns
plt.rcParams["figure.figsize"] = [8,6]
sns.set_style("darkgrid")
titanic_data = sns.load_dataset('titanic')
titanic_data = titanic_data[["embark_town", "age", "fare"]]
titanic_data.head()
titanic_data.isnull().mean()
# The output shows that the embark_town column is a
# categorical column that contains some missing values too. We
# will apply the missing value imputation to this column.
titanic_data.embark_town.fillna('Missing', inplace=True)
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Text(0, 0.5, 'Number of Passengers')
# After applying missing value imputation, plot the bar plot for
# the embark_town column. You can see that we have a very
# small, almost negligible plot for the missing column.
titanic_data.embark_town.value_counts().sort_values(ascending=False).plot.bar()
plt.xlabel('Embark Town')
plt.ylabel('Number of Passengers')

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