Debug School

rakesh kumar
rakesh kumar

Posted on • Updated on

Basic Preprocessing Machine Learning Commands

GENERAL COMMANDS

Read the CSV file into a DataFrame

df = pd.read_csv(file_path)
df
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Check Total no row and colunm

df.shape
print(f"The rows and columns in the dataset: {df.shape}")
print("shapes are:",x.shape)
print("shapes are:",y.shape)
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Display First 10 rows

df.head(10)
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Add new col in existing

df['new_column'] = df['column1'] + df['column2']
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Display last 5rows

df.tail()
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Display all colunm name

print(f"\nThe column headers in the dataset: {df.columns}")
print(f"\nThe column list in the dataset: {df.columns.tolist()}")
df.columns.tolist()
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Display all colunm name

list(x.columns)
list(df.columns)
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"Filtering a Pandas DataFrame in Python to Select Rows with Prices Between $1000 and $2500

df=df[df['price'] > 1000]
df=df[df['price'] < 2500]
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drop Unnamed colunm from existing dataset

df=df.drop('Unnamed: 0', axis=1)
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Display particular range of row

row_data = df.iloc[859:888]
row_data

df.iloc[10,:]  ==> select particular row all columns
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display colunm data type

df.info()  or   df.dtypes
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Find minimum value and maximum value of particular colunms

print("Min. Date:", df["Date"].min())==>find minimum value of particular colunms
print("Max. Date:", df["Date"].max())===>find maximum value of particular colunms
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Fill null value through mean,median and mode

df["totalcharges"].fillna(df["totalcharges"].mean())
mean_A = df['A'].mean()
df['A'].fillna(mean_A, inplace=True)

# Fill missing values in column 'B' using median
median_B = df['B'].median()
df['B'].fillna(median_B, inplace=True)

# Fill missing values in column 'C' using mode
mode_C = df['C'].mode()[0]  # Using [0] to get the first mode (in case there are multiple modes)
df['C'].fillna(mode_C, inplace=True)
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Find particular colunms minimum,maximum and average values

df["fixed acidity"].min()
df["fixed acidity"].max()
df["fixed acidity"].avg()
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Display or filter rows in a DataFrame (df) where the value in the 'TotalCharges' column is an empty string (" ")

df.loc[df['TotalCharges']==" "] is used to filter rows in a DataFrame (df) where the value in the 'TotalCharges' column is an empty string (" ").
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Replacing Empty Spaces in 'TotalCharges' Column with NaN in Pandas DataFrame

df['TotalCharges']=df['TotalCharges'].replace(" ",np.nan)
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Calculating the Mean of 'TotalCharges' in a Pandas DataFrame

mean_total_charges = np.mean(df['TotalCharges'])
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Identifying and Counting Missing Values in Each Column of a Pandas DataFrame

df.isnull().sum()
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sns.heatmap(df.isnull())
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df[column_name].isnull().sum()
df['Age'].isnull().sum()
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Determining the Number of Unique Values in the 'quality' Column of a Pandas DataFrame

df['quality'].nunique() ==single col how many unique value
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Counting the Occurrences of Each Unique Value in the 'quality' Column of a Pandas DataFrame

df['quality'].value_counts()  ===each value of col how many unique value

column_name = '0.00.1'
value_counts = df[column_name].value_counts()
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Counting the Number of Unique Values in Each Column of a Pandas DataFrame and Creating a 'No of Unique Frame

df.nunique().to_frame("no of unique frame") all col how many unique value
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Summary Analysis of a Pandas DataFrame

df.describe() == to show mean,median,std dev
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set_index() to Set the Date Column as the Index in a Pandas DataFrame"

df.set_index('Date', inplace=True)==> set date as index value
The set_index() function is used to set the DataFrame index using existing columns.
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Removing 'Close' and 'Customer_id' Columns for Train-Test Split in a Pandas DataFrame

df.drop(['Close'], axis=1, inplace=True)
df.drop(['Customer_id'], axis=1, inplace=True)
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Exploring Duplicate Rows in a Pandas DataFrame

df.duplicated().sum()
print("shapes are:",x.shape)
print("shapes are:",y.shape)

duplicate_rows = df[df.duplicated()]
print(duplicate_rows)

# Assuming df is your DataFrame
all_duplicate_rows = df[df.duplicated(keep=False)]
print(all_duplicate_rows)

df.drop_duplicates(inplace=True)
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The df.corr() function computes the correlation matrix, providing insights into relationships between variables

df_corr=df.corr()
df_corr

# Calculate the correlation between columns
correlation = df['YearsOld'].corr(df['Income'])
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write a code to print occurance of particular value in particular colunm

column_name = '0.00.1'
value_counts = df[column_name].value_counts()

# Print the count of occurrences of value 6
if 0.00 in value_counts.index:
    count_of_6 = value_counts.loc[0.00]
    print(f"Count of value 6 in {column_name}: {count_of_6}")
else:
    print(f"Value 6 not found in {column_name}")
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# Convert the DataFrame to a NumPy array
numpy_array = df.to_numpy()
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Counting the Unique Values in Each Column of a Pandas DataFrame"

for i in df.columns:
    print(df[i].value_counts())
    print("\n")
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Calculating Column-wise Sums in a Pandas DataFrame

for i in df.columns:
    print(df[i].sum())
    print("\n")
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Summing Values and Listing Them Column-wise in a Pandas DataFrame

column_sums_list = []
for i in df.columns:
    print(df[i].sum())
    print("\n")
    column_sum = df[i].sum()
    column_sums_list.append(column_sum)
print("List of Column Sums:")
print(column_sums_list)
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Creating Key-Value Pairs for Summed Values in Each Column of a Pandas DataFrame"

column_sums_list = []
for i in df.columns:
    print(df[i].value_counts())
    print("\n")
    column_sum = df[i].sum()
    column_sums_list.append({'Column': i, 'Sum': column_sum})
column_sums_list
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Summing Values, Creating Key-Value Pairs, and Listing Them in a Pandas DataFrame

column_sums_list = []
for i in df.columns:
    print(df[i].sum())
    print("\n")
    column_sum = df[i].sum()
    column_sums_list.append({'Column': i, 'Sum': column_sum})
print("List of Column Sums:")
print(column_sums_list)
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Creating a New Dataset with Sum and Max Values for Each Column

column_sums_list = []
for i in df.columns:
    print(df[i].value_counts())
    print("\n")
    column_sum = df[i].sum()
    column_sums_list.append({'Column': i, 'Sum': column_sum})
column_sums_list
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column_max_list = []
for i in df.columns:
    print(df[i].value_counts())
    print("\n")
    column_sum = df[i].max()
    column_max_list.append({'Column': i, 'max': column_sum})
column_max_list
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Exploring Different Join Operations on Pandas DataFrames
different-method-to-combine-the-dataframe-in-pandas

df_sums = pd.DataFrame(column_sums_list)
df_max = pd.DataFrame(column_max_list)
df_result = pd.merge(df_sums, df_max, on='Column')
df_result
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Creating and Renaming Columns in a Pandas DataFrame

# Create a new column by combining existing columns
df['FullName'] = df['Name'] + ' (' + df['Department'] + ')'
# Rename columns in the DataFrame
df.rename(columns={'Age': 'YearsOld', 'Salary': 'Income'}, inplace=True)
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Calculating Mean and Median in a Pandas DataFrame

mean_age = data['Age'].mean()
median_salary = data['Salary'].median()
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Merging and Concatenating DataFrames:
different-method-to-combine-the-dataframe-in-pandas

df_new=pd.concat([df,df_max],axis=1)
df_new


x=df.drop(['diagnosis'], axis=1)
list(x.columns)
df_colunms=pd.DataFrame(x.columns)

df_scores=pd.DataFrame(fit.scores)//selecy k feature in KNN folder
feature_score=pd.concat([df_columns,df_scores],axis=1)
df_new
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Grouping and Aggregating Data in a Pandas DataFrame

df = pd.DataFrame({'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar'],
                   'B': [1, 2, 3, 4, 5, 6]})
grouped = df.groupby('A').sum()
print(grouped)
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Combining Columns and Calculating Scores in a Pandas DataFrame

to conncatenate more than one colunm

result = df1.join(df2)
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df_new = pd.concat([df_new, df_columns1, df_columns2, df_columns3, df_scores1, df_scores2, df_scores3], axis=1)
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Conditional Column Summation: Using If-Else Conditions in Pandas DataFrame
DataFrame Column Sum Analysis with Threshold
you can apply if-else condition also

column_sums_list = []
for i in df.columns:
    print(df[i].value_counts())
    print("\n")
    column_sum = df[i].sum()
    if column_sum > 4000:
        column_sums_list.append({'Column': i, 'Sum': column_sum})
column_sums_list
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Identifying Categorical and Numerical Columns in a Pandas DataFrame

categorical_col = []

for i in df.dtypes.index:
    if df.dtypes[i] == "object":
        categorical_col.append(i)

print("Categorical Columns: ", categorical_col)
print("\n")
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#Checking for Numerical columns
numerical_col = []

for i in df.dtypes.index:
    if df.dtypes[i]!="object":
        numerical_col.append(i)

print("Numerical Columns:", numerical_col)
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Utilizing the StandardScaler to Standardize Features in a Pandas
Applying Standard Scaling to Ensure Standardized Mean and Deviation for Each Feature

from sklearn.preprocessing import StandardScaler

# Create an instance of the StandardScaler
scaler = StandardScaler()
x=pd.DataFrame(scaler.fit_transform(x),columns=x.columns)
x
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Calculating VIF to Assess Multicollinearity Among Features"
"Utilizing Statsmodels and Scikit-Learn for VIF Computation in Regression Analysis

vif = pd.DataFrame()

# Iterate through each column index in x
for i in range(len(x.columns)):
    # Calculate VIF for each feature
    vif_value = variance_inflation_factor(x.values, i)

    # Append VIF value and feature name to the DataFrame
    vif = vif.append({'VIF': vif_value, 'Features': x.columns[i]}, ignore_index=True)

# Display the resulting DataFrame
print(vif)
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   from statsmodels.stats.outliers_influence import variance_inflation_factor
    from sklearn.linear_model import LinearRegression
    vif = pd.DataFrame()    
    vif['VIF'] = [variance_inflation_factor(x.values, i) for i in range(len(x.columns))]
    vif['Features'] = x.columns
    vif
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Applying Z-Score Method to Identify and Filter Outliers in Feature Columns"
"Eliminating Data Points with Z-Score Greater than 3 for Improved Data Quality

out_features=df[['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar',
       'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density',
       'pH', 'sulphates', 'alcohol']]

z = np.abs(zscore(out_features))
z

np.where(z>3)

df1 = df[(z < 3).all(axis=1)]
df1.shape
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Utilizing Interquartile Range (IQR) to Identify and Remove Outliers"
"Applying IQR Method to Enhance Data Quality by Filtering Unusual Data Points"

Q1 = out_features.quantile(0.25)
Q3 = out_features.quantile(0.75)
IQR = Q3 - Q1

# Identify and remove outliers using IQR
df2 =df[~((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)] 
Q1
Q3
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Assessing the Percentage of Data Loss After Outlier Handling"
"Quantifying Impact on Data Size Following Outlier Treatment

data_loss_percentage = ((df.shape[0] - df1.shape[0]) / df.shape[0]) * 100
data_loss_percentage
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Cheatsheet 

df=df[df['price'] > 1000]
df=df[df['price'] < 2500]
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df=df.drop('Unnamed: 0', axis=1)

row_data = df.iloc[859:888]

df.iloc[10,:]
df.loc[df['TotalCharges']==" "] is used to filter rows in a DataFrame (df) where the value in the 'TotalCharges' column is an empty string (" ").
df['TotalCharges']=df['TotalCharges'].replace(" ",np.nan)
mean_total_charges = np.mean(df['TotalCharges'])
df.isnull().sum()
df['Age'].isnull().sum()
df['quality'].nunique()
df['quality'].value_counts()
df[i].sum()
column_sums_list.append({'Column': i, 'Sum': column_sum})
df_result = pd.merge(df_sums, df_max, on='Column')
median_salary = data['Salary'].median()
grouped = df.groupby('A').sum()
df.rename(columns={'Age': 'YearsOld', 'Salary': 'Income'}, inplace=True)

Summary

Check Total no row and colunm
Display First 10 rows
Add new col in existing
Display last 5rows
Display all colunm name
Filtering a Pandas DataFrame in Python to Select Rows with Prices Between $1000 and $2500
drop Unnamed colunm from existing dataset
Display particular range of row
select particular row all columns
display colunm data type
Fill null value through mean,median and mode
Display or filter rows in a DataFrame (df) where the value in the 'TotalCharges' column is an empty string (" ")
Replacing Empty Spaces in 'TotalCharges' Column with NaN in Pandas DataFrame
Calculating the Mean of 'TotalCharges' in a Pandas DataFrame
Identifying and Counting Missing Values in Each Column of a Pandas DataFrame
Identifying and Counting Missing Values in particular Column of a Pandas DataFrame
Determining the Number of Unique Values in the 'quality' Column of a Pandas DataFrame
Counting the Occurrences of Each Unique Value in the 'quality' Column of a Pandas DataFrame
Summary Analysis of a Pandas DataFrame
set_index() to Set the Date Column as the Index in a Pandas DataFrame"
Removing 'Close' and 'Customer_id' Columns for Train-Test Split in a Pandas DataFrame
print occurance of particular value in particular colunm
Counting the Unique Values in Each Column of a Pandas DataFrame
Creating a New Dataset with Sum and Max Values for Each Column
=========================or===========================
Creating a New Dataset with mean and median Values for Each Column

DataFrame Column Sum Analysis with Threshold

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