Pandas Ultimate Tutorial 2023 | Python Data Analysis

Published: 14 February 2023
on channel: Nod Coding Bootcamp

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NAVIGATE THE CONTENT!
0:00 Introduction to Codealong
0:37 Download Anaconda
1:04 Open Jupyter Notebook
2:50 Import libraries
PART I
3:28 DataFrame vs. Series
6:19 Inspecting Data in Pandas
9:16 Selecting Columns in Pandas
15:01 Methods vs. Attributes
18:41 Intuition or Common Sense in Pandas
23:53 Method Chaining in Pandas
PART II
27:08 loc[] and iloc[] to locate data in a DataFrame
34:35 Filter with Conditional Logic using loc
46:27 isin()
49:25 between()
52:52 Negating a condition with ~
54:29 Tricky Exercise to see if you understood ;)
59:38 Most Useful Methods for Data Analysis
1:00:30 query()
1:02:10 unique() and nunique()
1:04:24 rename() and reset_index()
1:09:27 nlargest() and nsmallest() with visualization
1:15:00 save a visualization as image with transparent background
1:16:59 value_counts() with visualization
1:21:03 sort_values()
1:24:12 The Aggregation Functions
1:27:33 quantile()
1:28:23 agg()
1:29:22 cumsum() with area plot
1:31:49 pd.cut() and pd.qcut()
1:38:25 apply()
1:45:45 corr() with a heatmap
1:48:06 mask upper part of heatmap
PART III
1:49:08 GroupBy, the most important thing!
1:56:44 Double Groupby
1:58:04 Double Groupby + unstack()
1:59:00 fixing legend in plot with get_level_values()
2:00:36 How to Pivot our Data
2:01:10 transpose() or T
2:01:38 pivot_table()
2:06:37 crosstab()
PART IV
2:10:50 Concatenating DataFrames with pd.concat()
2:14:39 pd.concat() using a loop
2:18:09 pd.concat() using a list comprehension
2:19:44 Joining DataFrames with merge()

df = pd.read_csv("https://raw.githubusercontent.com/caj...")

men60 = pd.read_csv("https://raw.githubusercontent.com/caj...")
women60 = pd.read_csv("https://raw.githubusercontent.com/caj...")

boys19 = pd.read_csv("https://raw.githubusercontent.com/caj...")
boys20 = pd.read_csv("https://raw.githubusercontent.com/caj...")

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