Rossmann Dataset Exploration¶

https://www.kaggle.com/competitions/rossmann-store-sales/

Todo¶

Analyze These Columns

'CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear', 'Promo2SinceWeek', 'Promo2SinceYear', 'PromoInterval'

Informations¶

Exploratory Data Analysis and Visualization¶

Objectives of exploratory data analysis:

• Study the distributions of individual columns (uniform, normal, exponential)
• Detect anomalies or errors in the data (e.g. missing/incorrect values)
• Study the relationship of target column with other columns (linear, non-linear etc.)
• Gather insights about the problem and the dataset
• Come up with ideas for preprocessing and feature engineering

Imports¶

In [ ]:

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

import seaborn as sns

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns

In [ ]:

%matplotlib inline

plt.style.use("Solarize_Light2")

%matplotlib inline plt.style.use("Solarize_Light2")

Load Datasets¶

In [ ]:

import gdown

# Replace with your Google Drive shareable link
url = 'https://drive.google.com/file/d/1PogAVq1OtCFCU37GKUN-LPfjuGML6npk/view?usp=sharing'

# Convert to the direct download link
file_id = url.split('/d/')[1].split('/')[0]
direct_url = f'https://drive.google.com/uc?id={file_id}'

# Download
gdown.download(direct_url, 'Rossmann.zip', quiet=False)

!unzip -o /content/Rossmann.zip -d /content/Rossmann

train_data = pd.read_csv("/content/Rossmann/train.csv", low_memory=False)
store_data = pd.read_csv("/content/Rossmann/store.csv")

test_data = pd.read_csv("/content/Rossmann/test.csv")
sample_submission_data = pd.read_csv("/content/Rossmann/sample_submission.csv")

import gdown # Replace with your Google Drive shareable link url = 'https://drive.google.com/file/d/1PogAVq1OtCFCU37GKUN-LPfjuGML6npk/view?usp=sharing' # Convert to the direct download link file_id = url.split('/d/')[1].split('/')[0] direct_url = f'https://drive.google.com/uc?id={file_id}' # Download gdown.download(direct_url, 'Rossmann.zip', quiet=False) !unzip -o /content/Rossmann.zip -d /content/Rossmann train_data = pd.read_csv("/content/Rossmann/train.csv", low_memory=False) store_data = pd.read_csv("/content/Rossmann/store.csv") test_data = pd.read_csv("/content/Rossmann/test.csv") sample_submission_data = pd.read_csv("/content/Rossmann/sample_submission.csv")

Downloading...
From: https://drive.google.com/uc?id=1PogAVq1OtCFCU37GKUN-LPfjuGML6npk
To: /content/Rossmann.zip
100%|██████████| 7.33M/7.33M [00:00<00:00, 177MB/s]

Archive:  /content/Rossmann.zip
  inflating: /content/Rossmann/sample_submission.csv  
  inflating: /content/Rossmann/store.csv  
  inflating: /content/Rossmann/test.csv  
  inflating: /content/Rossmann/train.csv  

In [ ]:

train_data["StateHoliday"] = train_data.StateHoliday.replace({0: '0'})
train_data.StateHoliday.unique()

train_data["StateHoliday"] = train_data.StateHoliday.replace({0: '0'}) train_data.StateHoliday.unique()

Out[ ]:

array(['0', 'a', 'b', 'c'], dtype=object)

Heads¶

In [ ]:

train_data.head()

train_data.head()

Out[ ]:

	Store	DayOfWeek	Date	Sales	Customers	Open	Promo	StateHoliday	SchoolHoliday
0	1	5	2015-07-31	5263	555	1	1	0	1
1	2	5	2015-07-31	6064	625	1	1	0	1
2	3	5	2015-07-31	8314	821	1	1	0	1
3	4	5	2015-07-31	13995	1498	1	1	0	1
4	5	5	2015-07-31	4822	559	1	1	0	1

In [ ]:

store_data.head()

store_data.head()

Out[ ]:

	Store	StoreType	Assortment	CompetitionDistance	CompetitionOpenSinceMonth	CompetitionOpenSinceYear	Promo2	Promo2SinceWeek	Promo2SinceYear	PromoInterval
0	1	c	a	1270.0	9.0	2008.0	0	NaN	NaN	NaN
1	2	a	a	570.0	11.0	2007.0	1	13.0	2010.0	Jan,Apr,Jul,Oct
2	3	a	a	14130.0	12.0	2006.0	1	14.0	2011.0	Jan,Apr,Jul,Oct
3	4	c	c	620.0	9.0	2009.0	0	NaN	NaN	NaN
4	5	a	a	29910.0	4.0	2015.0	0	NaN	NaN	NaN

Join Train & Stores¶

In [ ]:

data = train_data.merge(right=store_data, how="inner", on="Store").copy()
data.head(2)

data = train_data.merge(right=store_data, how="inner", on="Store").copy() data.head(2)

Out[ ]:

	Store	DayOfWeek	Date	Sales	Customers	Open	Promo	StateHoliday	SchoolHoliday	StoreType	Assortment	CompetitionDistance	CompetitionOpenSinceMonth	CompetitionOpenSinceYear	Promo2	Promo2SinceWeek	Promo2SinceYear	PromoInterval
0	1	5	2015-07-31	5263	555	1	1	0	1	c	a	1270.0	9.0	2008.0	0	NaN	NaN	NaN
1	2	5	2015-07-31	6064	625	1	1	0	1	a	a	570.0	11.0	2007.0	1	13.0	2010.0	Jan,Apr,Jul,Oct

In [ ]:

# if rows count not equals to each other you can use left join for merge.
train_data.shape, data.shape

# if rows count not equals to each other you can use left join for merge. train_data.shape, data.shape

Out[ ]:

((1017209, 9), (1017209, 18))

Reorder Columns¶

In [ ]:

targets = ["Sales", "Customers"]
columns = [col for col in list(data.columns) if col not in targets]
data = data.loc[:, columns + targets]
data.head(2)

targets = ["Sales", "Customers"] columns = [col for col in list(data.columns) if col not in targets] data = data.loc[:, columns + targets] data.head(2)

Out[ ]:

	Store	DayOfWeek	Date	Open	Promo	StateHoliday	SchoolHoliday	StoreType	Assortment	CompetitionDistance	CompetitionOpenSinceMonth	CompetitionOpenSinceYear	Promo2	Promo2SinceWeek	Promo2SinceYear	PromoInterval	Sales	Customers
0	1	5	2015-07-31	1	1	0	1	c	a	1270.0	9.0	2008.0	0	NaN	NaN	NaN	5263	555
1	2	5	2015-07-31	1	1	0	1	a	a	570.0	11.0	2007.0	1	13.0	2010.0	Jan,Apr,Jul,Oct	6064	625

Convert Date to DateTime¶

In [ ]:

data["Date"] = pd.to_datetime(data.Date)
data["Date"].dt.dayofyear.head(2)

data["Date"] = pd.to_datetime(data.Date) data["Date"].dt.dayofyear.head(2)

Out[ ]:

	Date
0	212
1	212

dtype: int32

Each Store Sales¶

Total sales per store

In [ ]:

sales_by_store = data.groupby("Store")["Sales"].sum().sort_values(ascending=False)
sales_by_store.head()

sales_by_store = data.groupby("Store")["Sales"].sum().sort_values(ascending=False) sales_by_store.head()

Out[ ]:

	Sales
Store
262	19516842
817	17057867
562	16927322
1114	16202585
251	14896870

dtype: int64

In [ ]:

sales_by_store.sum() == data["Sales"].sum()

sales_by_store.sum() == data["Sales"].sum()

Out[ ]:

np.True_

Total sales per year

In [ ]:

sales_by_y = data.groupby(["Store", data.Date.dt.year])[["Sales"]].sum().sort_values(by="Date" ,ascending=False)
sales_by_y.head()

sales_by_y = data.groupby(["Store", data.Date.dt.year])[["Sales"]].sum().sort_values(by="Date" ,ascending=False) sales_by_y.head()

Out[ ]:

		Sales
Store	Date
1107	2015	1105610
1106	2015	886655
1115	2015	1242048
1110	2015	865479
5	2015	816125

Total sales per month

In [ ]:

data["Year"] = data.Date.dt.year
data["Month"] = data.Date.dt.month
sales_by_ym = data.groupby(["Store", "Year", "Month"])[["Sales"]].sum().sort_values(by=["Store", "Year", "Month"], ascending=True)
sales_by_ym.head(12)

data["Year"] = data.Date.dt.year data["Month"] = data.Date.dt.month sales_by_ym = data.groupby(["Store", "Year", "Month"])[["Sales"]].sum().sort_values(by=["Store", "Year", "Month"], ascending=True) sales_by_ym.head(12)

Out[ ]:

			Sales
Store	Year	Month
1	2013	1	128431
		2	125271
		3	145169
		4	118009
		5	118225
		6	113411
		7	128032
		8	118320
		9	108867
		10	114641
		11	127596
		12	145168

Plots¶

Categorize Sales¶

very low, low, med, high and very high

In [ ]:

data.Sales.describe()

data.Sales.describe()

Out[ ]:

	Sales
count	1.017209e+06
mean	5.773819e+03
std	3.849926e+03
min	0.000000e+00
25%	3.727000e+03
50%	5.744000e+03
75%	7.856000e+03
max	4.155100e+04

dtype: float64

In [ ]:

# custom bins base on data.Sales.describe()
bins = [0, 3727, 5744, 7856, 15000, 42000]
labels = ['very low', 'low', 'med', 'high', 'very high']
sales_category = pd.cut(data['Sales'], bins=bins, labels=labels, include_lowest=True)

# custom bins base on data.Sales.describe() bins = [0, 3727, 5744, 7856, 15000, 42000] labels = ['very low', 'low', 'med', 'high', 'very high'] sales_category = pd.cut(data['Sales'], bins=bins, labels=labels, include_lowest=True)

Check for Missing and Duplicate Data¶

In [ ]:

data.isnull().sum(), data.duplicated().sum()

data.isnull().sum(), data.duplicated().sum()

Out[ ]:

(Store                             0
 DayOfWeek                         0
 Date                              0
 Open                              0
 Promo                             0
 StateHoliday                      0
 SchoolHoliday                     0
 StoreType                         0
 Assortment                        0
 CompetitionDistance            2642
 CompetitionOpenSinceMonth    323348
 CompetitionOpenSinceYear     323348
 Promo2                            0
 Promo2SinceWeek              508031
 Promo2SinceYear              508031
 PromoInterval                508031
 Sales                             0
 Customers                         0
 Year                              0
 Month                             0
 dtype: int64,
 np.int64(0))

Correlation Matrix¶

Heatmap¶

In [ ]:

data_temp = data.copy()

data_temp["Year"] = data["Date"].dt.year.copy()
data_temp["Month"] = data["Date"].dt.month.copy()
data_temp["Day"] = data["Date"].dt.day.copy()

fig, ax = plt.subplots(figsize=(15, 15))

sns.heatmap(data=data_temp[data_temp.select_dtypes(include=[np.number]).columns].corr(),
         annot=True,
         cmap="coolwarm",
         fmt=".2f",
         ax=ax)

plt.show()

data_temp = data.copy() data_temp["Year"] = data["Date"].dt.year.copy() data_temp["Month"] = data["Date"].dt.month.copy() data_temp["Day"] = data["Date"].dt.day.copy() fig, ax = plt.subplots(figsize=(15, 15)) sns.heatmap(data=data_temp[data_temp.select_dtypes(include=[np.number]).columns].corr(), annot=True, cmap="coolwarm", fmt=".2f", ax=ax) plt.show()

DF¶

In [ ]:

data_temp[data_temp.select_dtypes(include=[np.number]).columns].corr()["Sales"].sort_values(ascending=False)

data_temp[data_temp.select_dtypes(include=[np.number]).columns].corr()["Sales"].sort_values(ascending=False)

Out[ ]:

	Sales
Sales	1.000000
Customers	0.894711
Open	0.678472
Promo	0.452345
SchoolHoliday	0.085124
Promo2SinceWeek	0.059558
Month	0.048768
Year	0.023519
CompetitionOpenSinceYear	0.012659
Store	0.005126
Day	-0.011612
CompetitionDistance	-0.019229
Promo2SinceYear	-0.021127
CompetitionOpenSinceMonth	-0.028257
Promo2	-0.091040
DayOfWeek	-0.462125

dtype: float64

Recommended Plots¶

In [ ]:

data

data

Out[ ]:

	Store	DayOfWeek	Date	Open	Promo	StateHoliday	SchoolHoliday	StoreType	Assortment	CompetitionDistance	CompetitionOpenSinceMonth	CompetitionOpenSinceYear	Promo2	Promo2SinceWeek	Promo2SinceYear	PromoInterval	Sales	Customers	Year	Month
0	1	5	2015-07-31	1	1	0	1	c	a	1270.0	9.0	2008.0	0	NaN	NaN	NaN	5263	555	2015	7
1	2	5	2015-07-31	1	1	0	1	a	a	570.0	11.0	2007.0	1	13.0	2010.0	Jan,Apr,Jul,Oct	6064	625	2015	7
2	3	5	2015-07-31	1	1	0	1	a	a	14130.0	12.0	2006.0	1	14.0	2011.0	Jan,Apr,Jul,Oct	8314	821	2015	7
3	4	5	2015-07-31	1	1	0	1	c	c	620.0	9.0	2009.0	0	NaN	NaN	NaN	13995	1498	2015	7
4	5	5	2015-07-31	1	1	0	1	a	a	29910.0	4.0	2015.0	0	NaN	NaN	NaN	4822	559	2015	7
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1017204	1111	2	2013-01-01	0	0	a	1	a	a	1900.0	6.0	2014.0	1	31.0	2013.0	Jan,Apr,Jul,Oct	0	0	2013	1
1017205	1112	2	2013-01-01	0	0	a	1	c	c	1880.0	4.0	2006.0	0	NaN	NaN	NaN	0	0	2013	1
1017206	1113	2	2013-01-01	0	0	a	1	a	c	9260.0	NaN	NaN	0	NaN	NaN	NaN	0	0	2013	1
1017207	1114	2	2013-01-01	0	0	a	1	a	c	870.0	NaN	NaN	0	NaN	NaN	NaN	0	0	2013	1
1017208	1115	2	2013-01-01	0	0	a	1	d	c	5350.0	NaN	NaN	1	22.0	2012.0	Mar,Jun,Sept,Dec	0	0	2013	1

1017209 rows × 20 columns

Univariate Analysis (One feature at a time)¶

Histogram¶

figsize=(10, 6): Makes plots larger and more readable.

multiple='stack': Stacks bars by hue for better comparison.

palette='Set2': Uses a more distinct color palette.

edgecolor & linewidth: Adds outlines to bars for better separation.

tight_layout(): Prevents overlapping labels.

legend customization for clarity.

Rotated x-axis labels (useful for categorical or tightly packed values).

In [ ]:

# Set a clean, modern theme
sns.set(style="whitegrid")

# Loop through each column
for col in data.columns:
    plt.figure(figsize=(10, 6))  # Bigger figure for better readability

    # Plot with improved settings
    sns.histplot(data=data, x=col, hue=sales_category, multiple='stack', palette='Set2', edgecolor=".3", linewidth=0.5)

    # Add titles and labels
    plt.title(f"{col} Distribution by Sales Category", fontsize=14)
    plt.xlabel(col, fontsize=12)
    plt.ylabel("Frequency", fontsize=12)
    # plt.legend(title="Sales Category", fontsize=10, title_fontsize=11)

    # Rotate x-axis labels if needed
    plt.xticks(rotation=15)

    # Show plot
    plt.tight_layout()
    plt.show()

# Set a clean, modern theme sns.set(style="whitegrid") # Loop through each column for col in data.columns: plt.figure(figsize=(10, 6)) # Bigger figure for better readability # Plot with improved settings sns.histplot(data=data, x=col, hue=sales_category, multiple='stack', palette='Set2', edgecolor=".3", linewidth=0.5) # Add titles and labels plt.title(f"{col} Distribution by Sales Category", fontsize=14) plt.xlabel(col, fontsize=12) plt.ylabel("Frequency", fontsize=12) # plt.legend(title="Sales Category", fontsize=10, title_fontsize=11) # Rotate x-axis labels if needed plt.xticks(rotation=15) # Show plot plt.tight_layout() plt.show()

Boxplot¶

In [ ]:

# Set a clean, modern theme
sns.set(style="whitegrid")

# Loop through each column
for col in data.columns:
    plt.figure(figsize=(8, 4))  # Bigger figure for better readability

    # Plot with improved settings
    sns.boxplot(data=data, x=col)

    # Add titles and labels
    plt.title(f"{col} Box Plot by Sales Category", fontsize=14)
    plt.xlabel(col, fontsize=12)

    # Rotate x-axis labels if needed
    plt.xticks(rotation=15)

    # Show plot
    plt.tight_layout()
    plt.show()

# Set a clean, modern theme sns.set(style="whitegrid") # Loop through each column for col in data.columns: plt.figure(figsize=(8, 4)) # Bigger figure for better readability # Plot with improved settings sns.boxplot(data=data, x=col) # Add titles and labels plt.title(f"{col} Box Plot by Sales Category", fontsize=14) plt.xlabel(col, fontsize=12) # Rotate x-axis labels if needed plt.xticks(rotation=15) # Show plot plt.tight_layout() plt.show()

In [ ]:

# Set a clean, modern theme
sns.set(style="whitegrid")

# Loop through each column
for col in data.columns:
    plt.figure(figsize=(10, 6))  # Bigger figure for better readability

    # Plot with improved settings
    sns.boxplot(data=data, x=col, hue=sales_category, palette='Set2')

    # Add titles and labels
    plt.title(f"{col} Box Plot by Sales Category", fontsize=14)
    plt.xlabel(col, fontsize=12)

    # Rotate x-axis labels if needed
    plt.xticks(rotation=15)

    # Show plot
    plt.tight_layout()
    plt.show()

# Set a clean, modern theme sns.set(style="whitegrid") # Loop through each column for col in data.columns: plt.figure(figsize=(10, 6)) # Bigger figure for better readability # Plot with improved settings sns.boxplot(data=data, x=col, hue=sales_category, palette='Set2') # Add titles and labels plt.title(f"{col} Box Plot by Sales Category", fontsize=14) plt.xlabel(col, fontsize=12) # Rotate x-axis labels if needed plt.xticks(rotation=15) # Show plot plt.tight_layout() plt.show()

BarPlot¶

In [ ]:

data[(data["Open"] == 0) & (data["DayOfWeek"] == 7)]["Sales"].sum()

data[(data["Open"] == 0) & (data["DayOfWeek"] == 7)]["Sales"].sum()

Out[ ]:

np.int64(0)

In [ ]:

data[(data["Open"] == 1) & (data["DayOfWeek"] == 7)]["Sales"].sum()

data[(data["Open"] == 1) & (data["DayOfWeek"] == 7)]["Sales"].sum()

Out[ ]:

np.int64(29551433)

In [ ]:

sns.barplot(data, x="DayOfWeek", y="Sales")
plt.show()

sns.barplot(data, x="DayOfWeek", y="Sales") plt.show()

In [ ]:

sns.barplot(data=data[data["Open"] == 1], x="DayOfWeek", y="Sales")
plt.show()

sns.barplot(data=data[data["Open"] == 1], x="DayOfWeek", y="Sales") plt.show()

In [ ]:

sns.barplot(data=data, x='Promo', y='Sales')
plt.show()

sns.barplot(data=data, x='Promo', y='Sales') plt.show()

In [ ]:

for col in data_temp.columns[-3:]:
  sns.barplot(data=data_temp, x=col, y='Sales')
  plt.show()

for col in data_temp.columns[-3:]: sns.barplot(data=data_temp, x=col, y='Sales') plt.show()

In [ ]:

sns.barplot(data, x="Store", y="Sales")
plt.show()

sns.barplot(data, x="Store", y="Sales") plt.show()

Target Variable Analysis¶

In [ ]:

sns.histplot(data=data, x=data["Sales"], hue=sales_category, palette="Set2")
plt.show()

sns.histplot(data=data, x=data["Sales"], hue=sales_category, palette="Set2") plt.show()

In [ ]:

sns.histplot(data=data[data["Open"] == 1], x="Sales", hue=sales_category, palette="Set2")
plt.show()

sns.histplot(data=data[data["Open"] == 1], x="Sales", hue=sales_category, palette="Set2") plt.show()

Plot Analyze Columns with NaNs¶

In [ ]:

contain_nans_cols = ["CompetitionOpenSinceMonth", "CompetitionOpenSinceYear", "Promo2SinceWeek", "Promo2SinceYear", "PromoInterval", "CompetitionDistance"]

contain_nans_cols = ["CompetitionOpenSinceMonth", "CompetitionOpenSinceYear", "Promo2SinceWeek", "Promo2SinceYear", "PromoInterval", "CompetitionDistance"]

In [ ]:

# Loop through each column
for col in contain_nans_cols:
    plt.figure(figsize=(8, 4))  # Bigger figure for better readability

    # Plot with improved settings
    sns.boxplot(data=data, x=col)

    # Add titles and labels
    plt.title(f"{col} Box Plot by Sales Category", fontsize=14)
    plt.xlabel(col, fontsize=12)

    # Rotate x-axis labels if needed
    plt.xticks(rotation=15)

    # Show plot
    plt.tight_layout()
    plt.show()

# Loop through each column for col in contain_nans_cols: plt.figure(figsize=(8, 4)) # Bigger figure for better readability # Plot with improved settings sns.boxplot(data=data, x=col) # Add titles and labels plt.title(f"{col} Box Plot by Sales Category", fontsize=14) plt.xlabel(col, fontsize=12) # Rotate x-axis labels if needed plt.xticks(rotation=15) # Show plot plt.tight_layout() plt.show()

Outliers

CompetitionOpenSinceYear < 2000

CompetitionDistance > 16000

Analyze¶

'CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear', 'Promo2SinceWeek', 'Promo2SinceYear', 'PromoInterval'

In [ ]:

categorical_cols_temp = ['CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear', 'Promo2SinceWeek', 'Promo2SinceYear', 'PromoInterval']

categorical_cols_temp = ['CompetitionOpenSinceMonth', 'CompetitionOpenSinceYear', 'Promo2SinceWeek', 'Promo2SinceYear', 'PromoInterval']

Barplot¶

In [ ]:

for col in categorical_cols_temp:
  sns.barplot(data, x=col, y="Sales")
  plt.show()

for col in categorical_cols_temp: sns.barplot(data, x=col, y="Sales") plt.show()

'Promo2SinceYear' as numerical

'PromoInterval' as categorical

Explore Columns With Nans¶

In [ ]:

data[contain_nans_cols].info()

data[contain_nans_cols].info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1017209 entries, 0 to 1017208
Data columns (total 6 columns):
 #   Column                     Non-Null Count    Dtype  
---  ------                     --------------    -----  
 0   CompetitionOpenSinceMonth  693861 non-null   float64
 1   CompetitionOpenSinceYear   693861 non-null   float64
 2   Promo2SinceWeek            509178 non-null   float64
 3   Promo2SinceYear            509178 non-null   float64
 4   PromoInterval              509178 non-null   object 
 5   CompetitionDistance        1014567 non-null  float64
dtypes: float64(5), object(1)
memory usage: 46.6+ MB

In [ ]:

data[contain_nans_cols].nunique()

data[contain_nans_cols].nunique()

In [ ]:

data.nunique()

data.nunique()

Results¶

Tree-based models (like Decision Trees, Random Forest, XGBoost), they are usually robust to outliers. You might keep them if they carry meaningful information.

Outliers

CompetitionOpenSinceYear < 2000

CompetitionDistance > 16000

Customers > 1500

SchoolHoliday == 1 Binary

StateHoliday exclude(0)

Open == 0 Binary

In [ ]:

drop_cols = ["Store", "Date"]
target_cols = ["Sales", "Customers"]
scalar_cols = ["CompetitionDistance"]
binary_cols = ["Open", "Promo", "SchoolHoliday", "Promo2"]
categorical_cols = [col for col in data.columns if col not in drop_cols + target_cols + scalar_cols + binary_cols]

drop_cols = ["Store", "Date"] target_cols = ["Sales", "Customers"] scalar_cols = ["CompetitionDistance"] binary_cols = ["Open", "Promo", "SchoolHoliday", "Promo2"] categorical_cols = [col for col in data.columns if col not in drop_cols + target_cols + scalar_cols + binary_cols]

In [ ]:

data[categorical_cols].nunique()

data[categorical_cols].nunique()

Out[ ]:

	0
DayOfWeek	7
StateHoliday	4
StoreType	4
Assortment	3
CompetitionOpenSinceMonth	12
CompetitionOpenSinceYear	23
Promo2SinceWeek	24
Promo2SinceYear	7
PromoInterval	3
Year	3
Month	12

dtype: int64