Ma'lumotlar yuklanmoqda
Ma'lumotlar yuklanmoqda
x = 10
y = "Hello"- Data Types: - Integers:
x = 10- Floats:
y = 3.14- Strings:
name = "Alice"- Lists:
my_list = [1, 2, 3]- Dictionaries:
my_dict = {"key": "value"}- Tuples:
my_tuple = (1, 2, 3)- Control Structures: -
if, elif, elsestatements - Loops:
for i in range(5): print(i)- While loop:
while x < 5: print(x) x += 12. Importing Libraries - NumPy:
import numpy as np- Pandas:
import pandas as pd- Matplotlib:
import matplotlib.pyplot as plt- Seaborn:
import seaborn as sns3. NumPy for Numerical Data - Creating Arrays:
arr = np.array([1, 2, 3, 4])- Array Operations:
arr.sum() arr.mean()- Reshaping Arrays:
arr.reshape((2, 2))- Indexing and Slicing:
arr[0:2] # First two elements4. Pandas for Data Manipulation - Creating DataFrames:
df = pd.DataFrame({ 'col1': [1, 2, 3], 'col2': ['A', 'B', 'C'] })- Reading Data:
df = pd.read_csv('file.csv')- Basic Operations:
df.head() # First 5 rows df.describe() # Summary statistics df.info() # DataFrame info- Selecting Columns:
df['col1'] df[['col1', 'col2']]- Filtering Data:
df[df['col1'] > 2]- Handling Missing Data:
df.dropna() # Drop missing values df.fillna(0) # Replace missing values- GroupBy:
df.groupby('col2').mean()5. Data Visualization - Matplotlib:
plt.plot(df['col1'], df['col2']) plt.xlabel('X-axis') plt.ylabel('Y-axis') plt.title('Title') plt.show()- Seaborn:
sns.histplot(df['col1']) sns.boxplot(x='col1', y='col2', data=df)6. Common Data Operations - Merging DataFrames:
pd.merge(df1, df2, on='key')- Pivot Table:
df.pivot_table(index='col1', columns='col2', values='col3')- Applying Functions:
df['col1'].apply(lambda x: x*2)7. Basic Statistics - Descriptive Stats:
df['col1'].mean() df['col1'].median() df['col1'].std()- Correlation:
df.corr()#برنامه_نویسی #پایتون #Python #Programming ┏━━━━━━━━┓ 〓 @AIPyth0n ┗━━━━━━━━┛
x = 10
y = "Hello"- Data Types: - Integers:
x = 10- Floats:
y = 3.14- Strings:
name = "Alice"- Lists:
my_list = [1, 2, 3]- Dictionaries:
my_dict = {"key": "value"}- Tuples:
my_tuple = (1, 2, 3)- Control Structures: -
if, elif, elsestatements - Loops:
for i in range(5): print(i)- While loop:
while x < 5: print(x) x += 12. - NumPy:
import numpy as np- Pandas:
import pandas as pd- Matplotlib:
import matplotlib.pyplot as plt- Seaborn:
import seaborn as sns3. NumPy - Creating Arrays:
arr = np.array([1, 2, 3, 4])- Array Operations:
arr.sum() arr.mean()- Reshaping Arrays:
arr.reshape((2, 2))- Indexing and Slicing:
arr[0:2] # First two elements4. Pandas - Creating DataFrames:
df = pd.DataFrame({ 'col1': [1, 2, 3], 'col2': ['A', 'B', 'C'] })- Reading Data:
df = pd.read_csv('file.csv')- Basic Operations:
df.head() # First 5 rows df.describe() # Summary statistics df.info() # DataFrame info- Selecting Columns:
df['col1'] df[['col1', 'col2']]- Filtering Data:
df[df['col1'] > 2]- Handling Missing Data:
df.dropna() # Drop missing values df.fillna(0) # Replace missing values- GroupBy:
df.groupby('col2').mean()5. - Matplotlib:
plt.plot(df['col1'], df['col2']) plt.xlabel('X-axis') plt.ylabel('Y-axis') plt.title('Title') plt.show()- Seaborn:
sns.histplot(df['col1']) sns.boxplot(x='col1', y='col2', data=df)6. - Merging DataFrames:
pd.merge(df1, df2, on='key')- Pivot Table:
df.pivot_table(index='col1', columns='col2', values='col3')- Applying Functions:
df['col1'].apply(lambda x: x*2)7. - Descriptive Stats:
df['col1'].mean() df['col1'].median() df['col1'].std()- Correlation:
df.corr()#برنامه_نویسی #پایتون #Python #Programming ┏━━━━━━━━┓ 〓 @AIPyth0n ┗━━━━━━━━┛