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Please enable JavaScript in your browser settings or update your browser.Learn Checking Columns | Preprocessing Data: Part I
Analyzing and Visualizing Real-World Data
course content

Course Content

Analyzing and Visualizing Real-World Data

Analyzing and Visualizing Real-World Data

1. Preprocessing Data: Part I
Loading the DataData TypesColumns OverviewRemoving Characters: Method 1Removing Characters: Method 2Checking ColumnsReplacing Characters
2. Preprocessing Data: Part II
Analyzing ColumnsRemoving and ReplacingProcessing DatesWrong Convertation?Proper ConvertingThat's All?
3. Analyzing Data
Superficial AnalysisThe Most Profitable StoreDate RangeHow Periodic the Data is?The Most Profitable PeriodsThe Best CombinationHolidays EffectWhat Holidays are we Considering?The Most Profitable Holiday
4. Visualizing Data
First Look at the DataComparing DynamicsFinding RelationDoes Temperature Affect?Or is it Holiday Effect?Comparing ShopsAdding Holidays to Comparison

book
Checking Columns

Let's take a look at the intermediate results.


              
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# Loading the library
import pandas as pd

# Reading the data
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/72be5dde-f3e6-4c40-8881-e1d97ae31287/shops_data1.csv')

# Checking columns types
print(df.dtypes)
copy

So far, there are three columns left that have an object data type. Let's examine the 'CPI' column by displaying five random values.


              
12345678

            
# Loading the library
import pandas as pd

# Reading the data
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/72be5dde-f3e6-4c40-8881-e1d97ae31287/shops_data1.csv')

# Inspecting the 'CPI' column
print(df['CPI'].sample(5))
copy
question mark

What is wrong with the 'CDI' column?

Select the correct answer

Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 1. ChapterΒ 6

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course content

Course Content

Analyzing and Visualizing Real-World Data

Analyzing and Visualizing Real-World Data

1. Preprocessing Data: Part I
Loading the DataData TypesColumns OverviewRemoving Characters: Method 1Removing Characters: Method 2Checking ColumnsReplacing Characters
2. Preprocessing Data: Part II
Analyzing ColumnsRemoving and ReplacingProcessing DatesWrong Convertation?Proper ConvertingThat's All?
3. Analyzing Data
Superficial AnalysisThe Most Profitable StoreDate RangeHow Periodic the Data is?The Most Profitable PeriodsThe Best CombinationHolidays EffectWhat Holidays are we Considering?The Most Profitable Holiday
4. Visualizing Data
First Look at the DataComparing DynamicsFinding RelationDoes Temperature Affect?Or is it Holiday Effect?Comparing ShopsAdding Holidays to Comparison

book
Checking Columns

Let's take a look at the intermediate results.


              
12345678

            
# Loading the library
import pandas as pd

# Reading the data
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/72be5dde-f3e6-4c40-8881-e1d97ae31287/shops_data1.csv')

# Checking columns types
print(df.dtypes)
copy

So far, there are three columns left that have an object data type. Let's examine the 'CPI' column by displaying five random values.


              
12345678

            
# Loading the library
import pandas as pd

# Reading the data
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/72be5dde-f3e6-4c40-8881-e1d97ae31287/shops_data1.csv')

# Inspecting the 'CPI' column
print(df['CPI'].sample(5))
copy
question mark

What is wrong with the 'CDI' column?

Select the correct answer

Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 1. ChapterΒ 6
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