Python Data Analysis Bootcamp: Mastering Pandas and Matplotlib

Python Data Analysis Bootcamp: Mastering Pandas and Matplotlib (English Version)
Author: by Kender Contreras (Author) Format: Kindle Edition
Publication Date: 2023-11-13
Language: English
ASIN ‏ : ‎ B0CNBQ3GV3

In a world driven by information, data analysis has become an essential skill, but unfortunately, access to affordable and practical educational resources is often a challenge. Have you ever felt frustrated by the lack of affordable and practical training options? Do you want to learn data analysis in a way that feels relevant and applicable to real-world situations?

If so, you've come to the right place. This Data Analysis Bootcamp with Pandas and Matplotlib has been designed with people like you in mind, seeking to learn effectively and accessibly. Our motivation lies in providing affordable and high-quality educational opportunities for everyone, regardless of the budget you have.

This Bootcamp is not limited to abstract theory; it is designed to immerse you in exercises and real-world cases that you might face in your professional career. Here, you will not only learn the foundations of data analysis but also gain practical experience that will prepare you to tackle real-world challenges.

In this Bootcamp, you will get to know and learn how to use the following tools:

• pip install for library installation.
• import / as for library importation.
• pd.read to read and load files into dataframes.
• df.head(), df.info(), df.describe().
• df.duplicated().
• df.drop.
• df.isnull, df.fillna, df.dropna.
• df.to_csv.
• df.sum, df.max(), df.dtypes.
• pd.to_datetime.
• df.astype().
• df.str.replace.
• df.dt.to_period.
• df.groupby.
• .apply(''".format).
• df.nunique().
• .reset_index().
• .sort_values(ascending=False).
• .mean().
• .pct_change().
• .dt.day_name().
• creation of a pdf file with canvas.Canvas.
• c.drawString with all its parameters.
• c.drawImage with all its parameters.
• c.showPage().
• plt.figure, plt.title, plt.xlabel, plt.ylabel, plt.xticks(rotation=45), plt.tight_layout(), plt.savefig, plt.show().
• lambda.
• df.rename.
• inplace.
• Functions.
• Conditional if.
• c.save().

Shall we begin?

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