3.6) Pandas: Concatenate or Append Function

1.1) Why Python?

1.2) Why run Python code in Google Colab?

1.3) Why learn Python in DataCamp?

1.4) Why learn Python in Dataquest?

1.5) Introduction to Markdown in Google Colab

1.6) Learn Latex in Google Colab

1.7) Upload Data in Github to Open in Google Colab

2.1) NumPy: Data Types

2.2) NumPy: Creating Array

2.3) NumPy: Increment Operator

2.4) NumPy: Mean, Standard Deviation

3.1) Pandas: Introduction to Series

3.2) Pandas: DataFrame

3.3) Pandas: Reading Excel Files in Google Colab

3.4) Pandas: Reading CSV or Text Files in Google Colab

3.5) Pandas: Merging Dataframes

3.6) Pandas Concatenate or Append Function

3.7) Pandas: Pivoting from "Long" to "Wide" Format

3.8) Pandas: Mean of a Variable with Conditions

3.9) Pandas: Recode Categorical Variable

3.10) Pandas: Filter Columns and Rows

3.11) Load Excel/CSV Files into Google Colab

3.12) Series vs Dataframe vs MultiIndex (Panel)

4.1) Matplotlib: Introduction

4.2) Matplotlib: Boxplot and Histogram

4.3) Matplotlib: Scatterplot

4.4) Matplotlib: Figure vs Axis

5.1) Plotly: Dynamic Scatterplot with Clusters

5.2) Plotly: Dynamic Histogram with Vertical Line

5.3) Plotly: Bar Chart

6.1) Python: Function, Conditional Statement, Data Transformation

6.2) Python: Loop over List, String, Dictionary, and Numpy Array

6.3) Fibonacci: Recursion vs Loop

6.4) Python: Loop to Simulate Coin Tosses

6.5) Python Loop, List Comprehension, and Lambda Function

6.6) Python: *args vs **kwargs

6.7) What the Arrow Means in Python Function Definition?

7.1) Everything in Python is an Object

7.2) Learn How to Create a Class in Python

8.1) DataCamp Signal Assessment: Python Programming

[stephanieprice-cummings]

Could you explain how the levels and keys arguments can be used with the concatenate function?

[KMFAclan]

Is it possible to concatenate both a series and a dataframe with each other? If so, then how would you do it and would it still have a similar outcome like the examples in the video?

[bacharahmad]

i'm just noticing this for the first time, but why does dataframes allow for two columns to have the same name (in this case, both are 'A')? when i tried
df3 = pd.concat([df1, df2], axis=1)
for n in df3['A']:
print(n)

i got:
A
A

i know you can easily rename one of the columns or use iloc[ ], but i'm curious how dataframes treats columns with the same name (in other words, why did i get the results i got instead of the function printing all the rows)?

[yahiaabdelbar]

Why in the panda library they created the 'concat' function although the 'merge' by ID can get the same result?

[fayezsalame]

If the system results in NaN with axis = 1, then why is this allowed, should the axis option be omitted for these cases to avoid resulting in a NaN?