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Pandas First Steps

bookDescribing the Data

pandas offers the handy mean() method that calculates the average of all values for each column.

df = pd.read_csv(file.csv)
mean_values = df.mean()

You can also the same method to determine the average value for a specific column:

df = pd.read_csv(file.csv)
mean_values = df['column_name'].mean()

pandas also provides the mode() method, which identifies the most frequently occurring value in each column.

df = pd.read_csv(file.csv)
mode_values = df.mode()

To find the mode for a particular column, the same method is used:

df = pd.read_csv(file.csv)
mode_values = df['column_name'].mode()[0]
Note
Note

We use [0] after .mode() to extract the first value in case multiple modes exist. Without it, the method returns a full Series.

Another useful method in pandas is describe().

df = pd.read_csv(file.csv)
important_metrics = df.describe()

This method provides an overview of various metrics from the dataset, including:

  • Total number of entries;
  • Mean or average value;
  • Standard deviation;
  • The minimum and maximum values;
  • The 25th, 50th (median), and 75th percentiles.
Task

Swipe to start coding

You are given a DataFrame named wine_data.

  • Calculate the mean of the 'residual sugar' column and store the result in the residual_sugar_mean variable.
  • Calculate the mode of the 'fixed acidity' column and store the result in the fixed_acidity_mode variable.
  • Retrieve an overview of various statistics from wine_data and store the result in the described_data variable.

Solution

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SectionΒ 3. ChapterΒ 11
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bookDescribing the Data

pandas offers the handy mean() method that calculates the average of all values for each column.

df = pd.read_csv(file.csv)
mean_values = df.mean()

You can also the same method to determine the average value for a specific column:

df = pd.read_csv(file.csv)
mean_values = df['column_name'].mean()

pandas also provides the mode() method, which identifies the most frequently occurring value in each column.

df = pd.read_csv(file.csv)
mode_values = df.mode()

To find the mode for a particular column, the same method is used:

df = pd.read_csv(file.csv)
mode_values = df['column_name'].mode()[0]
Note
Note

We use [0] after .mode() to extract the first value in case multiple modes exist. Without it, the method returns a full Series.

Another useful method in pandas is describe().

df = pd.read_csv(file.csv)
important_metrics = df.describe()

This method provides an overview of various metrics from the dataset, including:

  • Total number of entries;
  • Mean or average value;
  • Standard deviation;
  • The minimum and maximum values;
  • The 25th, 50th (median), and 75th percentiles.
Task

Swipe to start coding

You are given a DataFrame named wine_data.

  • Calculate the mean of the 'residual sugar' column and store the result in the residual_sugar_mean variable.
  • Calculate the mode of the 'fixed acidity' column and store the result in the fixed_acidity_mode variable.
  • Retrieve an overview of various statistics from wine_data and store the result in the described_data variable.

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

SectionΒ 3. ChapterΒ 11
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single

close

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Completion rate improved to 3.03

bookDescribing the Data

Swipe to show menu

pandas offers the handy mean() method that calculates the average of all values for each column.

df = pd.read_csv(file.csv)
mean_values = df.mean()

You can also the same method to determine the average value for a specific column:

df = pd.read_csv(file.csv)
mean_values = df['column_name'].mean()

pandas also provides the mode() method, which identifies the most frequently occurring value in each column.

df = pd.read_csv(file.csv)
mode_values = df.mode()

To find the mode for a particular column, the same method is used:

df = pd.read_csv(file.csv)
mode_values = df['column_name'].mode()[0]
Note
Note

We use [0] after .mode() to extract the first value in case multiple modes exist. Without it, the method returns a full Series.

Another useful method in pandas is describe().

df = pd.read_csv(file.csv)
important_metrics = df.describe()

This method provides an overview of various metrics from the dataset, including:

  • Total number of entries;
  • Mean or average value;
  • Standard deviation;
  • The minimum and maximum values;
  • The 25th, 50th (median), and 75th percentiles.
Task

Swipe to start coding

You are given a DataFrame named wine_data.

  • Calculate the mean of the 'residual sugar' column and store the result in the residual_sugar_mean variable.
  • Calculate the mode of the 'fixed acidity' column and store the result in the fixed_acidity_mode variable.
  • Retrieve an overview of various statistics from wine_data and store the result in the described_data variable.

Solution

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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