pandas.tar.gz

pandas

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Python

pandas-with-sqlite3

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The course is a comprehensive guide to using pandas for data analysis. It covers a wide range of topics related to data manipulation, including filtering, merging, grouping, pivoting, and reshaping data.

In this course, you’ll learn about the best practices and efficient techniques for working with data. You’ll be provided with numerous examples of real-world data analysis problems and gain hands-on experience with the pandas library to solve them effectively. This course will also cover performance considerations and provide insights on common pitfalls to avoid when using pandas.

The course is suitable for both beginners and experienced users of pandas. It includes detailed explanations of pandas concepts and functions, as well as tips and tricks for optimizing your code. By the time you’re done with this course, you’ll be able to effectively handle data using pandas.

Effective Data Manipulation with pandas

```python
week    44    45    Total    
name
Alice   3     NaN    NaN
Bob     4     5      9
Carl    NaN   7      NaN
Total   NaN   NaN    NaN
```

```python
week    44    45    Total    
name
Alice   3     NaN    3
Bob     4     5      9
Carl    NaN   7      7
Total   7     12     19
```

```python
week    44    45    Total    
name
Alice   3     NaN   3
Bob     4     5     9
Carl    NaN   7     7
```

```python
week    44   45    
name
Alice   3    NaN    
Bob     4    5    
Carl    NaN  7    
Total   7    12
```

Look at the given DataFrame `df`:

```python
      name   age   rank  week        
0     Alice  12    3     44    
1     Bob    8     4     44
2     Carl   10    5     45
3     Carl   10    2     45
4     Bob    8     5     45
```

We want to create a pivot table. What will be the output of following command?

```python
pd.pivot_table(
data = df,
values = rank,
index = 'name',
columns = 'week',
aggfunc = 'sum',
margins = True,
margins_name = 'Total'
)
```

Look at the given DataFrame <code>df</code>:
<pre><code class="language-python"> name age rank week 
0 Alice 12 3 44 
1 Bob 8 4 44
2 Carl 10 5 45
3 Carl 10 2 45
4 Bob 8 5 45
</code></pre>
We want to create a pivot table. What will be the output of following command?
<pre><code class="language-python">pd.pivot_table(
data = df,
values = rank,
index = 'name',
columns = 'week',
aggfunc = 'sum',
margins = True,
margins_name = 'Total'
)
</code></pre>

`pd.pivot_table(df,index = ['Grade'],values = ['Marks'], aggfunc = np.mean)`

`pd.pivot_table(data=df,index=['Grade'], values= ['Marks'])`

`np.mean(pd.pivot_table(data=df,index=['Grade'], values= ['Marks']))`

`pd.pivot_table(data=df,index=['Grade'])`

**(Select all that apply.)** Consider a DataFrame that has the following columns: `student_id`, `student_name`, `Marks`, and `Grade`. We want to create a pivot table with `index` set to `grade` and want to see average marks per `grade`. Which of these are correct ways to get that information? 


(Select all that apply.) Consider a DataFrame that has the following columns: <code>student_id</code>, <code>student_name</code>, <code>Marks</code>, and <code>Grade</code>. We want to create a pivot table with <code>index</code> set to <code>grade</code> and want to see average marks per <code>grade</code>. Which of these are correct ways to get that information?

It creates a dummy variable encoding categorical information.  

It creates a dummy column with `NaN` by default. 

It creates a dummy row with `NaN` by default. 

What does the `get_dummies()` function do in pandas?


What does the <code>get_dummies()</code> function do in pandas?

Both `pivot_table()` and `crosstab()` functions accept only pandas DataFrame objects.


By default, the `pivot_table()` function displays mean values. The `crosstab()` displays the count of occurrence for each combination. 

`pivot_table()` can only have one index. 

`crosstab()` does not have an `aggfunc=` attribute. 

Which statement is correct about `pivot_table()` and `crosstab()` functions in pandas?

Which statement is correct about <code>pivot_table()</code> and <code>crosstab()</code> functions in pandas?

```python
a    min   max
1    1     4
3    NaN   NaN
```


```python
a    min   max
1    1     4
3    2     2
```

```python
     b           c    
a    min   max   min   max    
1    2     2     3     4
3    1     1     5     5
NaN  2     2     NaN   NaN
```

```python
     b           c    
a    min   max   min   max    
1    2     2     3     4
3    1     1     5     5
```

Look at the given DataFrame `df`:

```python
     a    b    c    
0    1    2    3    
1    1    2    4
2    3    1    5    
3    NaN  2    NaN

```

What will be the output of the following command?

```python
df.groupby('a').agg(['min','max'])
```

Look at the given DataFrame <code>df</code>:
<pre><code class="language-python"> a b c 
0 1 2 3 
1 1 2 4
2 3 1 5 
3 NaN 2 NaN

</code></pre>
What will be the output of the following command?
<pre><code class="language-python">df.groupby('a').agg(['min','max'])
</code></pre>

Quiz yourself on reshaping, pivot tables, and aggregations.

Introduction

Series Deep Dive

DataFrames

Manipulating Data

Wrapping Up

Appendix

Quiz: Reshaping, Pivot Tables, Aggregations