Python is a powerful programming language used for data analysis and processing. It offers a range of libraries and tools to perform data analysis processes. Here are the fundamental libraries and steps you can use for data analysis in Python:

**NumPy:**NumPy is a foundational library for scientific computations. It helps in creating and manipulating multidimensional data structures like arrays and matrices.**Pandas:**Pandas is an indispensable tool for data analysis and manipulation. It allows you to create, filter, group, and perform various data operations on DataFrames, which are akin to tables.**Matplotlib and Seaborn:**These libraries are used for data visualization. You can create various visual elements such as graphs, histograms, scatter plots, etc.**Scipy:**Scipy contains functions for scientific and technical computations. It encompasses statistics, optimization, linear algebra, differential equations, and more.**Jupyter Notebook:**Jupyter Notebook enables interactive and documented code writing. It’s ideal for step-by-step documentation of your data analysis process and visualizing results.**Scikit-Learn:**This library provides machine learning algorithms and data mining tools. It includes algorithms for classification, regression, clustering, dimensionality reduction, and more.**Statsmodels:**It aids in creating and analyzing statistical models. It’s used for statistical processes like regression analysis, time series analysis, and more.

A general process for performing data analysis in Python could be as follows:

1. Type the following commands in the command line window to install all packages.

```
pip install numpy
pip install scipy
pip install pandas
pip install matplotlib
pip install seaborn
```

2. Import the necessary libraries

```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
```

3. Load your data and convert it into a Pandas DataFrame

```
data = pd.read_csv("data.csv")
```

4. Explore the data

```
print(data.head()) # Display first few rows
print(data.info()) # General info about the data
print(data.describe()) # Basic statistical information
```

5. Clean and organize the data

```
data.dropna() # Drop missing values
data = data.drop_duplicates() # Drop duplicate rows
```

6. Visualize the data

```
sns.scatterplot(x="column1", y="column2", data=data)
plt.show()
```

7. Perform statistical analysis

```
correlation_matrix = data.corr() # Correlation matrix
```

8. Apply machine learning models (optional)

```
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
X = data[["feature1", "feature2"]]
y = data["target"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LinearRegression()
model.fit(X_train, y_train)
```

Data analysis in Python provides a powerful way to understand various datasets and derive insights. The specific tools and steps you use may vary based on your analysis goals and the type of data you’re dealing with.