Exploratory data analysis with Pandas#

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mlcourse.ai – Open Machine Learning Course

Author: Yury Kashnitsky. Translated and edited by Christina Butsko, Yuanyuan Pao, Anastasia Manokhina, Sergey Isaev and Artem Trunov. This material is subject to the terms and conditions of the Creative Commons CC BY-NC-SA 4.0 license. Free use is permitted for any non-commercial purpose.

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Source: Getty Images

Article outline#

1. Demonstration of the main Pandas methods#

There are many excellent tutorials available on Pandas and visual data analysis. If you already have a good understanding of these topics, you can move on to the third article in the series, which focuses on machine learning.

Pandas is a powerful Python library that makes it easy to analyze data. It is especially useful for working with data stored in table formats such as .csv, .tsv, or .xlsx. With Pandas, you can easily load, process, and analyze data using SQL-like commands. When used in conjunction with Matplotlib and Seaborn, Pandas provides a wealth of opportunities for visualizing and analyzing tabular data.

The core data structures in Pandas are Series and DataFrames. A Series is a one-dimensional indexed array of a single data type, while a DataFrame is a two-dimensional table where each column contains data of the same type. Think of a DataFrame as a collection of Series objects. DataFrames are ideal for representing real-world data, with each row representing an instance (such as an observation) and each column representing a feature of that instance.

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import numpy as np
import pandas as pd

pd.set_option("display.precision", 2)

We demonstrate the main methods in action by analyzing a dataset on the churn rate of telecom operator clients. Let’s read the data (using the read_csv method), and take a look at the first 5 lines using the head method:

# for Jupyter-book, we copy data from GitHub. Locally, to save Internet traffic,
# you can specify the data/ folder from the root of your cloned
# https://github.com/Yorko/mlcourse.ai repo
DATA_URL = "https://raw.githubusercontent.com/Yorko/mlcourse.ai/main/data/"
df = pd.read_csv(DATA_URL + "telecom_churn.csv")
df.head()
State Account length Area code International plan Voice mail plan Number vmail messages Total day minutes Total day calls Total day charge Total eve minutes Total eve calls Total eve charge Total night minutes Total night calls Total night charge Total intl minutes Total intl calls Total intl charge Customer service calls Churn
0 KS 128 415 No Yes 25 265.1 110 45.07 197.4 99 16.78 244.7 91 11.01 10.0 3 2.70 1 False
1 OH 107 415 No Yes 26 161.6 123 27.47 195.5 103 16.62 254.4 103 11.45 13.7 3 3.70 1 False
2 NJ 137 415 No No 0 243.4 114 41.38 121.2 110 10.30 162.6 104 7.32 12.2 5 3.29 0 False
3 OH 84 408 Yes No 0 299.4 71 50.90 61.9 88 5.26 196.9 89 8.86 6.6 7 1.78 2 False
4 OK 75 415 Yes No 0 166.7 113 28.34 148.3 122 12.61 186.9 121 8.41 10.1 3 2.73 3 False
Printing DataFrames in Jupyter notebooks

In Jupyter notebooks, Pandas DataFrames are printed as these pretty tables seen above while print(df.head()) is less nicely formatted. By default, Pandas displays 20 columns and 60 rows, so, if your DataFrame is bigger, use the set_option function as shown in the example below:

pd.set_option('display.max_columns', 100)
pd.set_option('display.max_rows', 100)

Recall that each row corresponds to one client, an instance, and columns are features of this instance.

Let’s have a look at data dimensionality, feature names, and feature types.

print(df.shape)
(3333, 20)

From the output, we can see that the table contains 3333 rows and 20 columns.

Now let’s try printing out column names using columns:

print(df.columns)
Index(['State', 'Account length', 'Area code', 'International plan',
       'Voice mail plan', 'Number vmail messages', 'Total day minutes',
       'Total day calls', 'Total day charge', 'Total eve minutes',
       'Total eve calls', 'Total eve charge', 'Total night minutes',
       'Total night calls', 'Total night charge', 'Total intl minutes',
       'Total intl calls', 'Total intl charge', 'Customer service calls',
       'Churn'],
      dtype='object')

We can use the info() method to output some general information about the dataframe:

print(df.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3333 entries, 0 to 3332
Data columns (total 20 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   State                   3333 non-null   object 
 1   Account length          3333 non-null   int64  
 2   Area code               3333 non-null   int64  
 3   International plan      3333 non-null   object 
 4   Voice mail plan         3333 non-null   object 
 5   Number vmail messages   3333 non-null   int64  
 6   Total day minutes       3333 non-null   float64
 7   Total day calls         3333 non-null   int64  
 8   Total day charge        3333 non-null   float64
 9   Total eve minutes       3333 non-null   float64
 10  Total eve calls         3333 non-null   int64  
 11  Total eve charge        3333 non-null   float64
 12  Total night minutes     3333 non-null   float64
 13  Total night calls       3333 non-null   int64  
 14  Total night charge      3333 non-null   float64
 15  Total intl minutes      3333 non-null   float64
 16  Total intl calls        3333 non-null   int64  
 17  Total intl charge       3333 non-null   float64
 18  Customer service calls  3333 non-null   int64  
 19  Churn                   3333 non-null   bool   
dtypes: bool(1), float64(8), int64(8), object(3)
memory usage: 498.1+ KB
None

bool, int64, float64 and object are the data types of our features. We see that one feature is logical (bool), 3 features are of type object, and 16 features are numeric. With this same method, we can easily see if there are any missing values. Here, there are none because each column contains 3333 observations, the same number of rows we saw before with shape.

We can change the column type with the astype method. Let’s apply this method to the Churn feature to convert it into int64:

df["Churn"] = df["Churn"].astype("int64")

The describe method shows basic statistical characteristics of each numerical feature (int64 and float64 types): number of non-missing values, mean, standard deviation, range, median, 0.25 and 0.75 quartiles.

df.describe()
Account length Area code Number vmail messages Total day minutes Total day calls Total day charge Total eve minutes Total eve calls Total eve charge Total night minutes Total night calls Total night charge Total intl minutes Total intl calls Total intl charge Customer service calls Churn
count 3333.00 3333.00 3333.00 3333.00 3333.00 3333.00 3333.00 3333.00 3333.00 3333.00 3333.00 3333.00 3333.00 3333.00 3333.00 3333.00 3333.00
mean 101.06 437.18 8.10 179.78 100.44 30.56 200.98 100.11 17.08 200.87 100.11 9.04 10.24 4.48 2.76 1.56 0.14
std 39.82 42.37 13.69 54.47 20.07 9.26 50.71 19.92 4.31 50.57 19.57 2.28 2.79 2.46 0.75 1.32 0.35
min 1.00 408.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 23.20 33.00 1.04 0.00 0.00 0.00 0.00 0.00
25% 74.00 408.00 0.00 143.70 87.00 24.43 166.60 87.00 14.16 167.00 87.00 7.52 8.50 3.00 2.30 1.00 0.00
50% 101.00 415.00 0.00 179.40 101.00 30.50 201.40 100.00 17.12 201.20 100.00 9.05 10.30 4.00 2.78 1.00 0.00
75% 127.00 510.00 20.00 216.40 114.00 36.79 235.30 114.00 20.00 235.30 113.00 10.59 12.10 6.00 3.27 2.00 0.00
max 243.00 510.00 51.00 350.80 165.00 59.64 363.70 170.00 30.91 395.00 175.00 17.77 20.00 20.00 5.40 9.00 1.00

In order to see statistics on non-numerical features, one has to explicitly indicate data types of interest in the include parameter.

df.describe(include=["object", "bool"])
State International plan Voice mail plan
count 3333 3333 3333
unique 51 2 2
top WV No No
freq 106 3010 2411

For categorical (type object) and boolean (type bool) features we can use the value_counts method. Let’s take a look at the distribution of Churn:

df["Churn"].value_counts()
Churn
0    2850
1     483
Name: count, dtype: int64

2850 users out of 3333 are loyal; their Churn value is 0. To calculate fractions, pass normalize=True to the value_counts function.

df["Churn"].value_counts(normalize=True)
Churn
0    0.86
1    0.14
Name: proportion, dtype: float64

Sorting#

A DataFrame can be sorted by the value of one of the variables (i.e columns). For example, we can sort by Total day charge (use ascending=False to sort in descending order):

df.sort_values(by="Total day charge", ascending=False).head()
State Account length Area code International plan Voice mail plan Number vmail messages Total day minutes Total day calls Total day charge Total eve minutes Total eve calls Total eve charge Total night minutes Total night calls Total night charge Total intl minutes Total intl calls Total intl charge Customer service calls Churn
365 CO 154 415 No No 0 350.8 75 59.64 216.5 94 18.40 253.9 100 11.43 10.1 9 2.73 1 1
985 NY 64 415 Yes No 0 346.8 55 58.96 249.5 79 21.21 275.4 102 12.39 13.3 9 3.59 1 1
2594 OH 115 510 Yes No 0 345.3 81 58.70 203.4 106 17.29 217.5 107 9.79 11.8 8 3.19 1 1
156 OH 83 415 No No 0 337.4 120 57.36 227.4 116 19.33 153.9 114 6.93 15.8 7 4.27 0 1
605 MO 112 415 No No 0 335.5 77 57.04 212.5 109 18.06 265.0 132 11.93 12.7 8 3.43 2 1

We can also sort by multiple columns:

df.sort_values(by=["Churn", "Total day charge"], ascending=[True, False]).head()
State Account length Area code International plan Voice mail plan Number vmail messages Total day minutes Total day calls Total day charge Total eve minutes Total eve calls Total eve charge Total night minutes Total night calls Total night charge Total intl minutes Total intl calls Total intl charge Customer service calls Churn
688 MN 13 510 No Yes 21 315.6 105 53.65 208.9 71 17.76 260.1 123 11.70 12.1 3 3.27 3 0
2259 NC 210 415 No Yes 31 313.8 87 53.35 147.7 103 12.55 192.7 97 8.67 10.1 7 2.73 3 0
534 LA 67 510 No No 0 310.4 97 52.77 66.5 123 5.65 246.5 99 11.09 9.2 10 2.48 4 0
575 SD 114 415 No Yes 36 309.9 90 52.68 200.3 89 17.03 183.5 105 8.26 14.2 2 3.83 1 0
2858 AL 141 510 No Yes 28 308.0 123 52.36 247.8 128 21.06 152.9 103 6.88 7.4 3 2.00 1 0

Indexing and retrieving data#

A DataFrame can be indexed in a few different ways.

To get a single column, you can use a DataFrame['Name'] construction. Let’s use this to answer a question about that column alone: what is the proportion of churned users in our dataframe?

df["Churn"].mean()
np.float64(0.14491449144914492)

14.5% is actually quite bad for a company; such a churn rate can make the company go bankrupt.

Boolean indexing with one column is also very convenient. The syntax is df[P(df['Name'])], where P is some logical condition that is checked for each element of the Name column. The result of such indexing is the DataFrame consisting only of the rows that satisfy the P condition on the Name column.

Let’s use it to answer the question:

What are the average values of numerical features for churned users?

Here we’l resort to an additional method select_dtypes to select all numeric columns.

df.select_dtypes(include=np.number)[df["Churn"] == 1].mean()
Account length            102.66
Area code                 437.82
Number vmail messages       5.12
Total day minutes         206.91
Total day calls           101.34
Total day charge           35.18
Total eve minutes         212.41
Total eve calls           100.56
Total eve charge           18.05
Total night minutes       205.23
Total night calls         100.40
Total night charge          9.24
Total intl minutes         10.70
Total intl calls            4.16
Total intl charge           2.89
Customer service calls      2.23
Churn                       1.00
dtype: float64

How much time (on average) do churned users spend on the phone during daytime?

df[df["Churn"] == 1]["Total day minutes"].mean()
np.float64(206.91407867494823)

What is the maximum length of international calls among loyal users (Churn == 0) who do not have an international plan?

df[(df["Churn"] == 0) & (df["International plan"] == "No")]["Total intl minutes"].max()
np.float64(18.9)

DataFrames can be indexed by column name (label) or row name (index) or by the serial number of a row. The loc method is used for indexing by name, while iloc() is used for indexing by number.

In the first case below, we say “give us the values of the rows with index from 0 to 5 (inclusive) and columns labeled from State to Area code (inclusive)”. In the second case, we say “give us the values of the first five rows in the first three columns” (as in a typical Python slice: the maximal value is not included).

df.loc[0:5, "State":"Area code"]
State Account length Area code
0 KS 128 415
1 OH 107 415
2 NJ 137 415
3 OH 84 408
4 OK 75 415
5 AL 118 510
df.iloc[0:5, 0:3]
State Account length Area code
0 KS 128 415
1 OH 107 415
2 NJ 137 415
3 OH 84 408
4 OK 75 415

If we need the first or the last line of the data frame, we can use the df[:1] or df[-1:] construction:

df[-1:]
State Account length Area code International plan Voice mail plan Number vmail messages Total day minutes Total day calls Total day charge Total eve minutes Total eve calls Total eve charge Total night minutes Total night calls Total night charge Total intl minutes Total intl calls Total intl charge Customer service calls Churn
3332 TN 74 415 No Yes 25 234.4 113 39.85 265.9 82 22.6 241.4 77 10.86 13.7 4 3.7 0 0

Applying Functions to Cells, Columns and Rows#

To apply functions to each column, use apply():

df.apply(np.max)
State                        WY
Account length              243
Area code                   510
International plan          Yes
Voice mail plan             Yes
Number vmail messages        51
Total day minutes         350.8
Total day calls             165
Total day charge          59.64
Total eve minutes         363.7
Total eve calls             170
Total eve charge          30.91
Total night minutes       395.0
Total night calls           175
Total night charge        17.77
Total intl minutes         20.0
Total intl calls             20
Total intl charge           5.4
Customer service calls        9
Churn                         1
dtype: object

The apply method can also be used to apply a function to each row. To do this, specify axis=1. Lambda functions are very convenient in such scenarios. For example, if we need to select all states starting with ‘W’, we can do it like this:

df[df["State"].apply(lambda state: state[0] == "W")].head()
State Account length Area code International plan Voice mail plan Number vmail messages Total day minutes Total day calls Total day charge Total eve minutes Total eve calls Total eve charge Total night minutes Total night calls Total night charge Total intl minutes Total intl calls Total intl charge Customer service calls Churn
9 WV 141 415 Yes Yes 37 258.6 84 43.96 222.0 111 18.87 326.4 97 14.69 11.2 5 3.02 0 0
26 WY 57 408 No Yes 39 213.0 115 36.21 191.1 112 16.24 182.7 115 8.22 9.5 3 2.57 0 0
44 WI 64 510 No No 0 154.0 67 26.18 225.8 118 19.19 265.3 86 11.94 3.5 3 0.95 1 0
49 WY 97 415 No Yes 24 133.2 135 22.64 217.2 58 18.46 70.6 79 3.18 11.0 3 2.97 1 0
54 WY 87 415 No No 0 151.0 83 25.67 219.7 116 18.67 203.9 127 9.18 9.7 3 2.62 5 1

The map method can be used to replace values in a column by passing a dictionary of the form {old_value: new_value} as its argument:

d = {"No": False, "Yes": True}
df["International plan"] = df["International plan"].map(d)
df.head()
State Account length Area code International plan Voice mail plan Number vmail messages Total day minutes Total day calls Total day charge Total eve minutes Total eve calls Total eve charge Total night minutes Total night calls Total night charge Total intl minutes Total intl calls Total intl charge Customer service calls Churn
0 KS 128 415 False Yes 25 265.1 110 45.07 197.4 99 16.78 244.7 91 11.01 10.0 3 2.70 1 0
1 OH 107 415 False Yes 26 161.6 123 27.47 195.5 103 16.62 254.4 103 11.45 13.7 3 3.70 1 0
2 NJ 137 415 False No 0 243.4 114 41.38 121.2 110 10.30 162.6 104 7.32 12.2 5 3.29 0 0
3 OH 84 408 True No 0 299.4 71 50.90 61.9 88 5.26 196.9 89 8.86 6.6 7 1.78 2 0
4 OK 75 415 True No 0 166.7 113 28.34 148.3 122 12.61 186.9 121 8.41 10.1 3 2.73 3 0

Almost the same thing can be done with the replace method.

Difference in treating values that are absent in the mapping dictionary

There's a slight difference. Еру `replace` method will not do anything with values not found in the mapping dictionary, while `map` will change them to NaNs).

a_series = pd.Series(['a', 'b', 'c'])
a_series.replace({'a': 1, 'b': 1})     # 1, 2, c
a_series.map({'a': 1, 'b': 2})     # 1, 2, NaN
0    1.0
1    2.0
2    NaN
dtype: float64

df = df.replace({"Voice mail plan": d})
df.head()
/var/folders/n0/1qj4_6k97ms4rnr_bsfq6j40018_cq/T/ipykernel_38604/1212166602.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
  df = df.replace({"Voice mail plan": d})
State Account length Area code International plan Voice mail plan Number vmail messages Total day minutes Total day calls Total day charge Total eve minutes Total eve calls Total eve charge Total night minutes Total night calls Total night charge Total intl minutes Total intl calls Total intl charge Customer service calls Churn
0 KS 128 415 False True 25 265.1 110 45.07 197.4 99 16.78 244.7 91 11.01 10.0 3 2.70 1 0
1 OH 107 415 False True 26 161.6 123 27.47 195.5 103 16.62 254.4 103 11.45 13.7 3 3.70 1 0
2 NJ 137 415 False False 0 243.4 114 41.38 121.2 110 10.30 162.6 104 7.32 12.2 5 3.29 0 0
3 OH 84 408 True False 0 299.4 71 50.90 61.9 88 5.26 196.9 89 8.86 6.6 7 1.78 2 0
4 OK 75 415 True False 0 166.7 113 28.34 148.3 122 12.61 186.9 121 8.41 10.1 3 2.73 3 0

Grouping#

In general, grouping data in Pandas works as follows:

df.groupby(by=grouping_columns)[columns_to_show].function()
  1. First, the groupby method divides the grouping_columns by their values. They become a new index in the resulting dataframe.

  2. Then, columns of interest are selected (columns_to_show). If columns_to_show is not included, all non groupby clauses will be included.

  3. Finally, one or several functions are applied to the obtained groups per selected columns.

Here is an example where we group the data according to the values of the Churn variable and display statistics of three columns in each group:

columns_to_show = ["Total day minutes", "Total eve minutes", "Total night minutes"]

df.groupby(["Churn"])[columns_to_show].describe(percentiles=[])
Total day minutes Total eve minutes Total night minutes
count mean std min 50% max count mean std min 50% max count mean std min 50% max
Churn
0 2850.0 175.18 50.18 0.0 177.2 315.6 2850.0 199.04 50.29 0.0 199.6 361.8 2850.0 200.13 51.11 23.2 200.25 395.0
1 483.0 206.91 69.00 0.0 217.6 350.8 483.0 212.41 51.73 70.9 211.3 363.7 483.0 205.23 47.13 47.4 204.80 354.9

Let’s do the same thing, but slightly differently by passing a list of functions to agg():

columns_to_show = ["Total day minutes", "Total eve minutes", "Total night minutes"]

df.groupby(["Churn"])[columns_to_show].agg(["mean", "std", "min", "max"])
Total day minutes Total eve minutes Total night minutes
mean std min max mean std min max mean std min max
Churn
0 175.18 50.18 0.0 315.6 199.04 50.29 0.0 361.8 200.13 51.11 23.2 395.0
1 206.91 69.00 0.0 350.8 212.41 51.73 70.9 363.7 205.23 47.13 47.4 354.9

Summary tables#

Suppose we want to see how the observations in our dataset are distributed in the context of two variables – Churn and International plan. To do so, we can build a contingency table using the crosstab method:

pd.crosstab(df["Churn"], df["International plan"])
International plan False True
Churn
0 2664 186
1 346 137
pd.crosstab(df["Churn"], df["Voice mail plan"], normalize=True)
Voice mail plan False True
Churn
0 0.60 0.25
1 0.12 0.02

We can see that most of the users are loyal and do not use additional services (International Plan/Voice mail).

This will resemble pivot tables to those familiar with Excel. And, of course, pivot tables are implemented in Pandas: the pivot_table method takes the following parameters:

  • values – a list of variables to calculate statistics for,

  • index – a list of variables to group data by,

  • aggfunc – what statistics we need to calculate for groups, e.g. sum, mean, maximum, minimum or something else.

Let’s take a look at the average number of day, evening, and night calls by area code:

df.pivot_table(
    ["Total day calls", "Total eve calls", "Total night calls"],
    ["Area code"],
    aggfunc="mean",
)
Total day calls Total eve calls Total night calls
Area code
408 100.50 99.79 99.04
415 100.58 100.50 100.40
510 100.10 99.67 100.60

DataFrame transformations#

Like many other things in Pandas, adding columns to a DataFrame is doable in many ways.

For example, if we want to calculate the total number of calls for all users, let’s create the total_calls Series and paste it into the DataFrame:

total_calls = (
    df["Total day calls"]
    + df["Total eve calls"]
    + df["Total night calls"]
    + df["Total intl calls"]
)
df.insert(loc=len(df.columns), column="Total calls", value=total_calls)
# loc parameter is the number of columns after which to insert the Series object
# we set it to len(df.columns) to paste it at the very end of the dataframe
df.head()
State Account length Area code International plan Voice mail plan Number vmail messages Total day minutes Total day calls Total day charge Total eve minutes Total eve calls Total eve charge Total night minutes Total night calls Total night charge Total intl minutes Total intl calls Total intl charge Customer service calls Churn Total calls
0 KS 128 415 False True 25 265.1 110 45.07 197.4 99 16.78 244.7 91 11.01 10.0 3 2.70 1 0 303
1 OH 107 415 False True 26 161.6 123 27.47 195.5 103 16.62 254.4 103 11.45 13.7 3 3.70 1 0 332
2 NJ 137 415 False False 0 243.4 114 41.38 121.2 110 10.30 162.6 104 7.32 12.2 5 3.29 0 0 333
3 OH 84 408 True False 0 299.4 71 50.90 61.9 88 5.26 196.9 89 8.86 6.6 7 1.78 2 0 255
4 OK 75 415 True False 0 166.7 113 28.34 148.3 122 12.61 186.9 121 8.41 10.1 3 2.73 3 0 359

It is possible to add a column more easily without creating an intermediate Series instance:

df["Total charge"] = (
    df["Total day charge"]
    + df["Total eve charge"]
    + df["Total night charge"]
    + df["Total intl charge"]
)
df.head()
State Account length Area code International plan Voice mail plan Number vmail messages Total day minutes Total day calls Total day charge Total eve minutes Total eve calls Total eve charge Total night minutes Total night calls Total night charge Total intl minutes Total intl calls Total intl charge Customer service calls Churn Total calls Total charge
0 KS 128 415 False True 25 265.1 110 45.07 197.4 99 16.78 244.7 91 11.01 10.0 3 2.70 1 0 303 75.56
1 OH 107 415 False True 26 161.6 123 27.47 195.5 103 16.62 254.4 103 11.45 13.7 3 3.70 1 0 332 59.24
2 NJ 137 415 False False 0 243.4 114 41.38 121.2 110 10.30 162.6 104 7.32 12.2 5 3.29 0 0 333 62.29
3 OH 84 408 True False 0 299.4 71 50.90 61.9 88 5.26 196.9 89 8.86 6.6 7 1.78 2 0 255 66.80
4 OK 75 415 True False 0 166.7 113 28.34 148.3 122 12.61 186.9 121 8.41 10.1 3 2.73 3 0 359 52.09

To delete columns or rows, use the drop method, passing the required indexes and the axis parameter (1 if you delete columns, and nothing or 0 if you delete rows). The inplace argument tells whether to change the original DataFrame. With inplace=False, the drop method doesn’t change the existing DataFrame and returns a new one with dropped rows or columns. With inplace=True, it alters the DataFrame.

# get rid of just created columns
df.drop(["Total charge", "Total calls"], axis=1, inplace=True)
# and here’s how you can delete rows
df.drop([1, 2]).head()
State Account length Area code International plan Voice mail plan Number vmail messages Total day minutes Total day calls Total day charge Total eve minutes Total eve calls Total eve charge Total night minutes Total night calls Total night charge Total intl minutes Total intl calls Total intl charge Customer service calls Churn
0 KS 128 415 False True 25 265.1 110 45.07 197.4 99 16.78 244.7 91 11.01 10.0 3 2.70 1 0
3 OH 84 408 True False 0 299.4 71 50.90 61.9 88 5.26 196.9 89 8.86 6.6 7 1.78 2 0
4 OK 75 415 True False 0 166.7 113 28.34 148.3 122 12.61 186.9 121 8.41 10.1 3 2.73 3 0
5 AL 118 510 True False 0 223.4 98 37.98 220.6 101 18.75 203.9 118 9.18 6.3 6 1.70 0 0
6 MA 121 510 False True 24 218.2 88 37.09 348.5 108 29.62 212.6 118 9.57 7.5 7 2.03 3 0

2. First attempt at predicting telecom churn#

Let’s see how churn rate is related to the International plan feature. We’ll do this using a crosstab contingency table and also through visual analysis with Seaborn (however, visual analysis will be covered more thoroughly in the next topic).

pd.crosstab(df["Churn"], df["International plan"], margins=True)
International plan False True All
Churn
0 2664 186 2850
1 346 137 483
All 3010 323 3333
# some imports to set up plotting
import matplotlib.pyplot as plt

# !pip install seaborn
import seaborn as sns

# import some nice vis settings
sns.set()
# Graphics in the Retina format are more sharp and legible
%config InlineBackend.figure_format = 'retina'
sns.countplot(x="International plan", hue="Churn", data=df);
../../_images/84b291ecbf1e61b3bb008a6386a7b9205a9f6fa5758bf964463f3b5542885e45.png

We observe that the churn rate is significantly higher with the International Plan. This is a noteworthy finding. Perhaps, high and poorly managed expenses for international calls cause conflicts and result in discontent among the telecom operator’s customers.

Next, let’s look at another important feature – Customer service calls. Let’s also make a summary table and a picture.

pd.crosstab(df["Churn"], df["Customer service calls"], margins=True)
Customer service calls 0 1 2 3 4 5 6 7 8 9 All
Churn
0 605 1059 672 385 90 26 8 4 1 0 2850
1 92 122 87 44 76 40 14 5 1 2 483
All 697 1181 759 429 166 66 22 9 2 2 3333
sns.countplot(x="Customer service calls", hue="Churn", data=df);
../../_images/b05603953c4d7870ab01a5c42d98478a5d692905f13799794b4588b8da26138e.png

Although it’s not so obvious from the summary table, it’s easy to see from the above plot that the churn rate increases sharply from 4 customer service calls and above.

Now let’s add a binary feature to our DataFrame – Customer service calls > 3. And once again, let’s see how it relates to churn.

df["Many_service_calls"] = (df["Customer service calls"] > 3).astype("int")

pd.crosstab(df["Many_service_calls"], df["Churn"], margins=True)
Churn 0 1 All
Many_service_calls
0 2721 345 3066
1 129 138 267
All 2850 483 3333
sns.countplot(x="Many_service_calls", hue="Churn", data=df);
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Let’s construct another contingency table that relates Churn with both the International plan and the freshly created Many_service_calls feature.

pd.crosstab(df["Many_service_calls"] & df["International plan"], df["Churn"], margins=True)
Churn 0 1 All
row_0
False 2841 464 3305
True 9 19 28
All 2850 483 3333

Thus, by predicting that a customer will not remain loyal (Churn=1) if they have made more than 3 calls to the service center AND have added the International Plan, and predicting Churn=0 otherwise (and “otherwise” here means negation, i.e. Many_service_calls <= 3 OR International Plan is not added ), we anticipate an accuracy of 85.8% (we will only be incorrect 464 + 9 times, look at the contingency table above; and \(1 - \frac{464 + 9}{3333} \approx 85.8\%\)). This 85.8% accuracy, obtained through such straightforward reasoning, serves as a useful starting point (baseline) for the development of future machine learning models.

As we move on through this course, recall that, before the advent of machine learning, the data analysis process looked something like this. Let’s recap what we’ve covered:

  • The share of loyal clients in the dataset is 85.5%. The most naive model that always predicts a “loyal customer” on such data will guess right in about 85.5% of all cases. That is, the proportion of correct answers (accuracy) of subsequent models should be no less than this number, and will hopefully be significantly higher;

  • With the help of a simple prediction that can be expressed by the following formula: International plan = True & Customer Service calls > 3 => Churn = 1, else Churn = 0, we can expect a guessing rate of 85.8%, which is just above 85.5%. Subsequently, we’ll talk about decision trees and figure out how to find such rules automatically based only on the input data;

  • We got these two baselines without applying machine learning, and they’ll serve as the starting point for our subsequent models. If it turns out that with enormous effort, we increase accuracy by only 0.5%, persay, then possibly we are doing something wrong, and it suffices to confine ourselves to a simple “if-else” model with two conditions;

  • Before training complex models, it is recommended to wrangle the data a bit, make some plots, and check simple assumptions. Moreover, in business applications of machine learning, they usually start with simple solutions and then experiment with more complex ones.

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3. Useful resources#