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Please enable JavaScript in your browser settings or update your browser.Describing the Data | Analyzing the Data
Pandas First Steps
course content

Course Content

Pandas First Steps

Pandas First Steps

1. The Very First Steps
What is pandas? SeriesCreating a SeriesDataFrameSeries QuizCreating a DataFrame QuizAdding a New ColumnInserting a New ColumnDeleting a Row/Column Matching the FunctionsWorking with ColumnsExtracting Columns Quiziloc BasicsUsing iloc
2. Reading Files in Pandas
CSV FilesTXT FilesWorking with Files QuizAccessing the Columns
3. Analyzing the Data
Viewing the DataHead QuizHead and Tail QuizInformation on the DataColumn Names and Data TypesFinding Null ValuesIdentifying Null Values QuizDropping Null ValuesFilling Null ValuesNull Values QuizDescribing the DataCalculating Descriptive Statisticsmax() and min()Statistical Operations Quizsum() and count()Unique Values

bookDescribing the Data

Pandas offers the handy mean() method that calculates the average of all values for each column. Here's how you can utilize it:

To determine the average value for a specific column, you can follow this method:

Pandas also provides the mode() method, which identifies the most frequently occurring value in each column. See the example below:

To find the mode for a particular column, you can do this:

Another useful method in pandas is describe(). Here's how to use it:

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

Retrieve the following from the dataframe named data_frame:

  • Total entries for each column;
  • Average value for each column;
  • Standard deviation for each column;
  • Minimum value for each column;
  • Maximum value for each column;
  • 25th, 50th, and 75th percentiles for each column.

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Section 3. Chapter 11
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bookDescribing the Data

Pandas offers the handy mean() method that calculates the average of all values for each column. Here's how you can utilize it:

To determine the average value for a specific column, you can follow this method:

Pandas also provides the mode() method, which identifies the most frequently occurring value in each column. See the example below:

To find the mode for a particular column, you can do this:

Another useful method in pandas is describe(). Here's how to use it:

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

Retrieve the following from the dataframe named data_frame:

  • Total entries for each column;
  • Average value for each column;
  • Standard deviation for each column;
  • Minimum value for each column;
  • Maximum value for each column;
  • 25th, 50th, and 75th percentiles for each column.

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!

Section 3. Chapter 11
toggle bottom row

bookDescribing the Data

Pandas offers the handy mean() method that calculates the average of all values for each column. Here's how you can utilize it:

To determine the average value for a specific column, you can follow this method:

Pandas also provides the mode() method, which identifies the most frequently occurring value in each column. See the example below:

To find the mode for a particular column, you can do this:

Another useful method in pandas is describe(). Here's how to use it:

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

Retrieve the following from the dataframe named data_frame:

  • Total entries for each column;
  • Average value for each column;
  • Standard deviation for each column;
  • Minimum value for each column;
  • Maximum value for each column;
  • 25th, 50th, and 75th percentiles for each column.

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!

Pandas offers the handy mean() method that calculates the average of all values for each column. Here's how you can utilize it:

To determine the average value for a specific column, you can follow this method:

Pandas also provides the mode() method, which identifies the most frequently occurring value in each column. See the example below:

To find the mode for a particular column, you can do this:

Another useful method in pandas is describe(). Here's how to use it:

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

Retrieve the following from the dataframe named data_frame:

  • Total entries for each column;
  • Average value for each column;
  • Standard deviation for each column;
  • Minimum value for each column;
  • Maximum value for each column;
  • 25th, 50th, and 75th percentiles for each column.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 3. Chapter 11
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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