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💡 Newskategorie: Programmierung
🔗 Quelle: dev.to
Day 5 of 100 Days Data Science Bootcamp from noob to expert.
GitHub link: Complete-Data-Science-Bootcamp
Main Post: Complete-Data-Science-Bootcamp
Recap Day 4
Yesterday we have studied in detail about NumPy in Python.
Let's Start
Pandas is a powerful data analysis and manipulation library in Python. It allows you to easily access, select, and manipulate data in your dataset. In this post, you'll learn how to use Pandas to create a gradebook for tracking student grades. You'll learn how to read in data from a CSV file, manipulate the data, and create a report of the grades. You'll also learn how to handle missing values and prepare your data for visualization. By the end of this course, you'll be able to efficiently use Pandas to manage and analyze your data.
Let's say we have a dataset of student grades in a CSV file called "grades.csv". The first step in exploring this dataset with Pandas is to read it into a Pandas DataFrame. We can do this using the read_csv() function:
import pandas as pd
df = pd.read_csv('grades.csv')
df
| name | grade | |
|---|---|---|
| 0 | John | 89.0 |
| 1 | Mary | 95.0 |
| 2 | Emily | 77.0 |
| 3 | Michael | 82.0 |
| 4 | Rachel | NaN |
- Now that we have our DataFrame, we can start exploring the data. Let's say we want to access the grades of a specific student. We can do this by selecting the row of the student and then selecting the 'grade' column:
student_name = 'John'
grade = df[df['name'] == student_name]['grade']
print(grade)
0 89.0 Name: grade, dtype: float64
- We can also select a specific column by its label using the '[]' operator:
grades = df['grade']
print(grades)
0 89.0 1 95.0 2 77.0 3 82.0 4 NaN Name: grade, dtype: float64
- If we want to select multiple columns, we can pass a list of column labels to the '[]' operator:
student_info = df[['name', 'grade']]
print(student_info)
name grade 0 John 89.0 1 Mary 95.0 2 Emily 77.0 3 Michael 82.0 4 Rachel NaN
- Now let's say we have some missing values in our dataset. We can handle these missing values using the fillna() function:
df = df.fillna(-1)
df
| name | grade | |
|---|---|---|
| 0 | John | 89.0 |
| 1 | Mary | 95.0 |
| 2 | Emily | 77.0 |
| 3 | Michael | 82.0 |
| 4 | Rachel | -1.0 |
This will replace all missing values with -1.
This is basic overview of pandas. Now we will go deep dive into it.
1. Importing and reading in data:
- read in data from a variety of sources, such as a CSV file:
import pandas as pd
df = pd.read_csv('people_data.csv')
df
#Or a Excel file:
# df = pd.read_excel('data.xlsx')
| Name | Age | Gender | |
|---|---|---|---|
| 0 | John | 20 | Male |
| 1 | Jane | 30 | Female |
| 2 | Bob | 40 | Male |
| 3 | Alice | 50 | Female |
2. Inspecting data:
Once you have your data in a pandas DataFrame, you can use various methods to inspect it.
For example, you can view the first few rows of the data using the head() method:
df.head()
| Name | Age | Gender | |
|---|---|---|---|
| 0 | John | 20 | Male |
| 1 | Jane | 30 | Female |
| 2 | Bob | 40 | Male |
| 3 | Alice | 50 | Female |
You can also view the column names and data types using the info() method:
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 4 entries, 0 to 3 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Name 4 non-null object 1 Age 4 non-null int64 2 Gender 4 non-null object dtypes: int64(1), object(2) memory usage: 224.0+ bytes
3. Selecting data:
You can select specific columns or rows of data using the [] operator or the loc and iloc attributes.
For example, to select the "Name" and "Age" columns, you can use the following code:
df[['Name', 'Age']]
| Name | Age | |
|---|---|---|
| 0 | John | 20 |
| 1 | Jane | 30 |
| 2 | Bob | 40 |
| 3 | Alice | 50 |
To select rows with a specific value in a certain column, you can use the loc attribute:
df.loc[df['Gender'] == 'Female']
| Name | Age | Gender | |
|---|---|---|---|
| 1 | Jane | 30 | Female |
| 3 | Alice | 50 | Female |
4. Manipulating data:
You can use various methods to manipulate data in a pandas DataFrame.
For example, you can add a new column by assigning a value to a new column name:
df['County'] = ["India", "USA", "India", "Canada"]
df
| Name | Age | Gender | County | |
|---|---|---|---|---|
| 0 | John | 20 | Male | India |
| 1 | Jane | 30 | Female | USA |
| 2 | Bob | 40 | Male | India |
| 3 | Alice | 50 | Female | Canada |
You can also drop columns or rows using the drop() method:
newdf = df.drop('County', axis=1) # drop a column
newdf
| Name | Age | Gender | |
|---|---|---|---|
| 0 | John | 20 | Male |
| 1 | Jane | 30 | Female |
| 2 | Bob | 40 | Male |
| 3 | Alice | 50 | Female |
newdf1 = df.drop(df[df['Age'] < 35].index, inplace=True) # drop rows with Age < 18
df
| Name | Age | Gender | County | |
|---|---|---|---|---|
| 2 | Bob | 40 | Male | India |
| 3 | Alice | 50 | Female | Canada |
5. Grouping and aggregating data:
You can group data by specific values and apply an aggregation function using the groupby() method and the apply() function:
import numpy as np
groupdf = df.groupby('Gender')['Age'].apply(np.mean) # group by Gender and calculate mean Age
groupdf
Gender Female 50.0 Male 40.0 Name: Age, dtype: float64
6. Merging and joining data:
You can merge or join data from multiple DataFrames using the merge() function or the concat() function.
For example, to merge two DataFrames based on a common column, you can use the following code:
df1 = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
'A': ['A0', 'A1', 'A2', 'A3'],
'B': ['B0', 'B1', 'B2', 'B3']})
df1
| key | A | B | |
|---|---|---|---|
| 0 | K0 | A0 | B0 |
| 1 | K1 | A1 | B1 |
| 2 | K2 | A2 | B2 |
| 3 | K3 | A3 | B3 |
df2 = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],
'C': ['C0', 'C1', 'C2', 'C3'],
'D': ['D0', 'D1', 'D2', 'D3']})
df2
| key | C | D | |
|---|---|---|---|
| 0 | K0 | C0 | D0 |
| 1 | K1 | C1 | D1 |
| 2 | K2 | C2 | D2 |
| 3 | K3 | C3 | D3 |
merged_df = pd.merge(df1, df2, on='key')
merged_df
| key | A | B | C | D | |
|---|---|---|---|---|---|
| 0 | K0 | A0 | B0 | C0 | D0 |
| 1 | K1 | A1 | B1 | C1 | D1 |
| 2 | K2 | A2 | B2 | C2 | D2 |
| 3 | K3 | A3 | B3 | C3 | D3 |
To concatenate data horizontally (i.e. adding columns), you can use the concat() function:
concat_df = pd.concat([df1, df2], axis=1)
concat_df
| key | A | B | key | C | D | |
|---|---|---|---|---|---|---|
| 0 | K0 | A0 | B0 | K0 | C0 | D0 |
| 1 | K1 | A1 | B1 | K1 | C1 | D1 |
| 2 | K2 | A2 | B2 | K2 | C2 | D2 |
| 3 | K3 | A3 | B3 | K3 | C3 | D3 |
7. Handling missing data:
It's common to encounter missing data in real-world datasets. Pandas provides various methods to handle missing data, such as filling missing values with a specific value or dropping rows with missing values.
To fill missing values with a specific value, you can use the fillna() method:
data = {'Name': ['John', 'Jane', 'Bob', 'Alice'],
'Age': [20, 30, 40, np.nan],
'Gender': ['Male', 'Female', 'Male', 'Female']}
df = pd.DataFrame(data)
df
| Name | Age | Gender | |
|---|---|---|---|
| 0 | John | 20.0 | Male |
| 1 | Jane | 30.0 | Female |
| 2 | Bob | 40.0 | Male |
| 3 | Alice | NaN | Female |
df['Age'].fillna(value='22', inplace=True)
df
| Name | Age | Gender | |
|---|---|---|---|
| 0 | John | 20.0 | Male |
| 1 | Jane | 30.0 | Female |
| 2 | Bob | 40.0 | Male |
| 3 | Alice | 22 | Female |
To drop rows with missing values, you can use the dropna() method:
df.dropna(inplace=True)
8. Working with dates and times:
Pandas has built-in support for working with dates and times.
You can convert a column of strings to datetime objects using the to_datetime() function:
df['Date'] = pd.to_datetime(df['Date'])
You can then extract specific parts of the datetime, such as the year or month, using the dt attribute:
df['Year'] = df['Date'].dt.year
df['Month'] = df['Date'].dt.month
9. Advanced operations:
There are many more advanced operations that you can perform with pandas, such as pivot tables, time series analysis, and machine learning. Here are a few more code snippets to help you explore these topics:
To create a pivot table, you can use the pivot_table() function:
pivot_table = df.pivot_table(index='Column1', columns='Column2', values='Column3', aggfunc=np.mean)
To perform time series analysis, you can use the resample() method to resample data at a different frequency:
resampled_df = df.resample('D').mean() # resample to daily frequency
9. Visualizing data:
You can use the plot() method to create various types of plots, such as bar plots, scatter plots, and line plots:
df.plot(x='X Column', y='Y Column', kind='scatter') # scatter plot
df.plot(x='X Column', y='Y Column', kind='bar') # bar plot
df.plot(x='X Column', y='Y Column') # line plot
In conclusion, pandas is a powerful and versatile library for data manipulation and analysis in Python. With its wide range of built-in functions and methods, pandas makes it easy to work with a variety of data sources, perform complex data operations, and visualize results. Whether you're a beginner or an experienced data scientist, pandas is an essential tool for any data-related project.
Exercise Question you will find in the exercise notebook of Day 5 on GitHub.
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