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Pandas Curriculum: 6 Chapters, 15 Lessons — pelatihan.org
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Pandas Curriculum
Learn Pandas online with 15 structured lessons, code examples, and 75 quiz questions.
15 lessons
6 chapters
75 questions
Summaries and key points are free to preview. Full lessons, code examples, and quizzes require a subscription.
01
Pandas Tutorial
8 lessons
01
Pandas HOME
Pandas is a Python library for data manipulation and analysis that provides a Series and DataFrame structure. With pandas, tasks like reading files, filtering lines, combining…
02
Pandas Intro
Pandas are made to work with table-shaped data, similar to spreadsheets but running in Python. Its main structure, the DataFrame, supports filtering, merging, and aggregating…
03
Pandas Getting Started
Pandas are installed with pipes and then imported with the abbreviation pd into the Python script. Once installed, you can create a DataFrame from a dictionary or read a data…
04
Pandas Series
A series is a one-dimensional structure of a pandas that holds a set of indexed values, similar to an array but with a label. Each element can be accessed via index labels or…
05
Pandas DataFrames
A DataFrame is a two-dimensional table containing rows and columns that are the main structure of the pandas. You can make it from a dictionary, list, or read a file, then access…
06
Pandas Read CSV
Pandas.read_csv reads the CSV file into a DataFrame with a single line of code, complete with column separators and adjustable data types. Once processed, the results can be…
07
Pandas Read JSON
In addition to CSV, pandas can read JSON-formatted data via read_json so that the JSON structure of an array of objects directly becomes a DataFrame. This reading is useful for…
08
Pandas Analyzing Data
Once the data is loaded, the pandas provide a variety of quick ways to understand the content, from head, describe, info, to value_counts. These methods provide a picture of the…
02
Cleaning Data
5 lessons
01
Cleaning Data
Real data almost always has problems like empty values, wrong formatting, or duplicate data. Data cleaning is the process of detecting and correcting such problems so that the…
02
Cleaning Empty Cells
A blank cell can be dealt with in three main ways: leave it, delete the row, or fill with a replacement value. The panda offers the dropna to remove the row and the fillna to…
03
Cleaning Wrong Format
Sometimes the numeric column contains text-type values so the calculation fails. For that, data type conversion needs to be done, for example with pd.to_numeric or astype. If the…
04
Cleaning Wrong Data
The wrong data can be values beyond the reasonable range, such as negative age or extreme values that are not possible. To deal with it, you need to find a suspicious line and…
05
Removing Duplicates
The duplicate line makes the data count double so that the statistics become biased. The pandas provide duplicated to detect the twin lines and drop_duplicates to discard them.…
03
Correlations
1 lessons
01
Pandas Correlations
Correlation measures how closely linear relationships between two numeric variables, ranging from -1 to 1. The function corr() on the DataFrame produces a correlation matrix that…
04
Plotting
1 lessons
01
Pandas Plotting
The pandas have a built-in plot method that utilizes Matplotlib to create graphics directly from DataFrame or Series. Various types of graphs are available, such as lines, bars,…