pandas

• Pandas unique data

• Filter Pandas Dataframe with multiple conditions

• How to Use Pandas to Check Multiple Columns for a Condition

• How to Iterate Over Rows with Pandas – Loop Through a Dataframe

• Different ways to iterate over rows in Pandas Dataframe

Iterate Dataframe

for index, row in df.iterrows():
    print(row['name'], row['age'])

for col in df.columns:
    print(col)

using index

for ind in df.index:
    print(df['Name'][ind], df['Stream'][ind])

using loc[] or iloc[]

for i in range(len(df)):
    print(df.loc[i, "Name"], df.loc[i, "Age"])
    
for i in range(len(df)):
    print(df.iloc[i, 0], df.iloc[i, 2])

using apply method

print(df.apply(lambda row: row["Name"] + " " +
               str(row["Percentage"]), axis=1))

Filter dataframe

Pandas where() method: Filtering with Conditions

Filter Rows that Contain a Specific String

df[df["team"].str.contains("A")]

Filter Rows that Contain a String in a List

df[df["team"].str.contains("A|B")]

Filter rows that conatin a partial string

#identify partial string to look for
keep= ["Wes"]

#filter for rows that contain the partial string "Wes" in the conference column
df[df.conference.str.contains('|'.join(keep))]

Filter by a List of strings

# Filter the DataFrame by a list of string values in the "City" column
filtered_df = df[df["City"].isin(["Los Angeles", "Chicago"])]
# Another
display(dataFrame.loc[(dataFrame['Salary']>=100000) & (dataFrame['Age']< 40) & (dataFrame['JOB'].str.startswith('D')),
                    ['Name','JOB']])

Get Column Name

# Example 1: Get the list of all column names from headers
column_names = list(df.columns.values)

# Example 2: Get the list of all column names from headers
column_names = df.columns.values.tolist()

# Example 3: Using list(df) to get the column headers as a list
column_names = list(df.columns)

# Example 4: Using list(df) to get the list of all Column Names
column_names = list(df)

# Example 5: Dataframe show all columns sorted list
column_names=sorted(df)

# Example 6: Get all Column Header Labels as List
for column_headers in df.columns: 
    print(column_headers)
    
column_names = df.keys().values.tolist()

# Example 7: Get all numeric columns
numeric_columns = df._get_numeric_data().columns.values.tolist()

# Example 8: Simple Pandas Numeric Columns Code
numeric_columns=df.dtypes[df.dtypes == "int64"].index.values.tolist()