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- [📘 Day 25](#%f0%9f%93%98-day-25)
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- [Pandas](#pandas)
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- [Importing pandas](#importing-pandas)
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- [Creating Pandas Series with default index](#creating-pandas-series-with-default-index)
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- [Creating Pandas Series with custom index](#creating-pandas-series-with-custom-index)
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- [Creating Pandas Series from a dictionary](#creating-pandas-series-from-a-dictionary)
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- [Creating a constant pandas series](#creating-a-constant-pandas-series)
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- [Creating a pandas series using linspace](#creating-a-pandas-series-using-linspace)
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- [Importing Pandas](#importing-pandas)
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- [Creating Pandas Series with Default Index](#creating-pandas-series-with-default-index)
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- [Creating Pandas Series with Custom Index](#creating-pandas-series-with-custom-index)
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- [Creating Pandas Series from a Dictionary](#creating-pandas-series-from-a-dictionary)
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- [Creating a Constant Pandas Series](#creating-a-constant-pandas-series)
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- [Creating a Pandas Series Using Linspace](#creating-a-pandas-series-using-linspace)
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- [DataFrames](#dataframes)
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- [Creating DataFrames from list of lists](#creating-dataframes-from-list-of-lists)
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- [Creating DataFrame using Dictionary](#creating-dataframe-using-dictionary)
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- [Creating DataFrams from list of dictionaries](#creating-dataframs-from-list-of-dictionaries)
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- [Reading CSV File using pandas](#reading-csv-file-using-pandas)
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- [Creating DataFrames from List of Lists](#creating-dataframes-from-list-of-lists)
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- [Creating DataFrame Using Dictionary](#creating-dataframe-using-dictionary)
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- [Creating DataFrams from List of Dictionaries](#creating-dataframs-from-list-of-dictionaries)
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- [Reading CSV File Using Pandas](#reading-csv-file-using-pandas)
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- [Data Exploration](#data-exploration)
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- [Modifying DataFrame](#modifying-dataframe)
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- [Create a DataFrame](#create-a-dataframe)
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- [Adding new column](#adding-new-column)
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- [Modifying column values](#modifying-column-values)
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- [Formating DataFrame column](#formating-dataframe-column)
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- [Checking data types of Column values](#checking-data-types-of-column-values)
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- [Adding a New Column](#adding-a-new-column)
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- [Modifying Column Values](#modifying-column-values)
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- [Formating DataFrame Column](#formating-dataframe-column)
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- [Checking Data Types of Column Values](#checking-data-types-of-column-values)
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- [Boolean Indexing](#boolean-indexing)
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- [Exercises: Day 25](#exercises-day-25)
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# 📘 Day 25
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## Pandas
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Pandas is an open source,high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
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Pandas adds data structures and tools designed to work with table-like data which is Series and Data Frames
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Pandas is an open source, high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
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Pandas adds data structures and tools designed to work with table-like data which is Series and Data Frames.
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Pandas provides tools for data manipulation: reshaping, merging, sorting, slicing, aggregation and imputation.
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For Mac:
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```py
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pip install conda
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conda install pandas
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```
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For Windows:
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```py
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pip install conda
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pip install pandas
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```
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Pandas data structure is based on *Series* and *DataFrames*
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A series is a column and a DataFrame is a multidimensional table made up of collection of series. In order to create a pandas series we should use numpy to create a one dimensional arrays or a python list.
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Let's see an example of a series:
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Names pandas Series
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Names Pandas Series
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Countries Series
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@ -66,20 +75,20 @@ Cities Series
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As you can see, pandas series is just one column data. If we want to have multiple columns we use data frames. The example below shows pandas DataFrames.
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As you can see, pandas series is just one column of data. If we want to have multiple columns we use data frames. The example below shows pandas DataFrames.
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Let's see, an example of a pandas data frame:
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Data from is a collection of rows and columns. Look at the table below it has many columns than the above
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Data frame is a collection of rows and columns. Look at the table below; it has many more columns than the example above:
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Next, we will see how to import pandas and how to create Series and DataFrames using pandas
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## Importing pandas
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## Importing Pandas
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```python
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@ -87,25 +96,24 @@ import pandas as pd # importing pandas as pd
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import numpy as np # importing numpy as np
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```
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### Creating Pandas Series with default index
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### Creating Pandas Series with Default Index
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```python
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nums = [1, 2, 3, 4,5]
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s = pd.Series(nums)
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s
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print(s)
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```
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```sh
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0 1
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1 2
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2 3
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3 4
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4 5
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dtype: int64
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```
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### Creating Pandas Series with custom index
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```python
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nums = [1, 2, 3, 4, 5]
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s = pd.Series(nums, index=[1, 2, 3, 4, 5])
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s
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print(s)
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```
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```sh
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1 1
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2 2
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3 3
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4 4
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5 5
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dtype: int64
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```
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```python
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fruits = ['Orange','Banana','Mangao']
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fruits = pd.Series(fruits, index=[1, 2, 3])
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fruits
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print(fruits)
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```
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```sh
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1 Orange
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2 Banana
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3 Mangao
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dtype: object
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```
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### Creating Pandas Series from a dictionary
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### Creating Pandas Series from a Dictionary
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```python
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@ -157,48 +165,48 @@ dct = {'name':'Asabeneh','country':'Finland','city':'Helsinki'}
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```python
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s = pd.Series(dct)
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s
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print(s)
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```
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```sh
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name Asabeneh
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country Finland
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city Helsinki
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dtype: object
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```
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### Creating a constant pandas series
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### Creating a Constant Pandas Series
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```python
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s = pd.Series(10, index = [1, 2,3])
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s
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print(s)
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```
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```sh
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1 10
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2 10
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3 10
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dtype: int64
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```
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### Creating a pandas series using linspace
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### Creating a Pandas Series Using Linspace
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```python
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s = pd.Series(np.linspace(5, 20, 10)) # linspace(starting, end, items)
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s
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print(s)
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```
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```sh
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0 5.000000
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1 6.666667
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2 8.333333
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8 18.333333
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9 20.000000
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dtype: float64
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```
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## DataFrames
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Pandas data frames can be created in different ways.
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### Creating DataFrames from list of lists
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### Creating DataFrames from List of Lists
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```python
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['John', 'Sweden', 'Stockholm']
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]
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df = pd.DataFrame(data, columns=['Names','Country','City'])
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df
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print(df)
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```
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```html
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<div>
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<style scoped>
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.dataframe tbody tr th:only-of-type {
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</tbody>
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</table>
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</div>
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```
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### Creating DataFrame using Dictionary
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### Creating DataFrame Using Dictionary
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```python
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```
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### Creating DataFrams from list of dictionaries
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### Creating DataFrams from List of Dictionaries
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```python
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@ -411,7 +418,7 @@ df
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## Reading CSV File using pandas
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## Reading CSV File Using Pandas
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```python
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@ -491,7 +498,7 @@ df.head() # give five rows we can increase the number of rows by passing argumen
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As you can see the csv file has three rows:Gender, Height and Weight. But we don't know the number of rows. Let's use shape meathod.
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As you can see the csv file has three rows: Gender, Height and Weight. But we don't know the number of rows. Let's use shape meathod.
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```python
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@ -590,7 +597,7 @@ df.tail() # tails give the last five rows, we can increase the rows by passing a
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Now, lets get specif colums using the column key
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Now, lets get specific column using the column key
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@ -703,7 +710,7 @@ weights.describe()
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```python
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df.describe() # describe can also give statistical information from a datafrom
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df.describe() # describe can also give statistical information from a dataFrame
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```
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@ -790,7 +797,8 @@ Modifying a DataFrame
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* we can change the data type of column values from DataFrame
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### Create a DataFrame
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All the time, first we import the necessary packages. Now, lets import pandas and numpy two best friends ever.
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All the time, first we import the necessary packages. Now, lets import pandas and numpy, two best friends ever.
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```python
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@ -859,7 +867,7 @@ Adding column in DataFrame is like adding a key in dictionary.
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First let's use the previous example to create a DataFrame. After we create the DataFrame, we will start modifying the columns and column values.
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### Adding new column
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### Adding a New Column
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Let's add a weight column in the DataFrame
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