pandas notes 1

convert float colum to int col + pandas

You can convert a float column to an integer column in a pandas DataFrame using the astype() function. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'float_column': [1.1, 2.2, 3.3, 4.4, 5.5]}
df = pd.DataFrame(data)

# Convert float column to int column
df['int_column'] = df['float_column'].astype(int)

print(df)

This will create a new column called 'int_column' in the DataFrame df, where the values are converted from float to integer. Note that the conversion simply truncates the decimal part of the float numbers.

pandas one column value as a list

If you want to convert a pandas DataFrame column into a Python list, you can simply use the tolist() method of the pandas Series. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'column_name': [1, 2, 3, 4, 5]}
df = pd.DataFrame(data)

# Convert column to list
column_list = df['column_name'].tolist()

print(column_list)

This will print the values of the 'column_name' column as a Python list.

how-to-avoid-pandas-creating-an-index-in-a-saved-csv

When you save a DataFrame to a CSV file using pandas, by default, it includes the index unless you specify not to include it. You can avoid pandas creating an index in a saved CSV file by using the index=False parameter in the to_csv() function. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'column1': [1, 2, 3],
        'column2': ['a', 'b', 'c']}

df = pd.DataFrame(data)

# Save DataFrame to CSV without index
df.to_csv('output.csv', index=False)

In this example, index=False prevents pandas from saving the index to the CSV file. Now, the saved CSV file will only contain the data columns without the index column.

renaming-column-names-in-pandas

To rename column names in pandas, you can use the rename() function. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'old_column1': [1, 2, 3],
        'old_column2': ['a', 'b', 'c']}

df = pd.DataFrame(data)

# Rename columns
df = df.rename(columns={'old_column1': 'new_column1', 'old_column2': 'new_column2'})

print(df)

In this example, rename() function is used to rename the columns. You pass a dictionary to columns parameter where keys are the old column names and values are the new column names you want to assign. After renaming, you assign the result back to the DataFrame or use inplace=True parameter to rename the columns in place without having to assign back.

pandas-show-entire-rows-without-truncation

To display entire rows in pandas DataFrame without truncation, you can adjust the display options using pd.set_option(). Specifically, you can set the display.max_colwidth and display.max_rows options to None to display the entire content of each cell and all rows, respectively. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'column1': ['Lorem ipsum dolor sit amet, consectetur adipiscing elit.',
                    'Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.',
                    'Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.']}
df = pd.DataFrame(data)

# Set display options to show entire rows without truncation
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_rows', None)

print(df)

In this example, display.max_colwidth is set to None to display the entire content of each cell, and display.max_rows is also set to None to display all rows without truncation. As a result, the entire rows of the DataFrame will be shown without any truncation.

add column after specific column + pandas

You can add a new column after a specific column in a pandas DataFrame by using the insert() method. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3],
        'B': ['a', 'b', 'c'],
        'C': [True, False, True]}

df = pd.DataFrame(data)

# New column data
new_column_data = [4, 5, 6]

# Insert new column after column 'B'
df.insert(loc=df.columns.get_loc('B') + 1, column='D', value=new_column_data)

print(df)

In this example, the insert() method is used to add a new column named 'D' after column 'B'. The loc parameter specifies the index position where the new column should be inserted. We use get_loc() to find the index position of column 'B' and then add 1 to it to insert the new column after 'B'. Finally, we specify the name and data for the new column using the column and value parameters, respectively.

get rows from and to pandas

To get rows from and to specific indices in a pandas DataFrame, you can use slicing. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3, 4, 5],
        'B': ['a', 'b', 'c', 'd', 'e']}

df = pd.DataFrame(data)

# Get rows from index 1 to index 3 (inclusive)
subset_df = df.iloc[1:4]

print(subset_df)

In this example, iloc[] is used to select rows based on their integer location. The slicing 1:4 retrieves rows from index 1 to index 3 (inclusive). Note that the index in Python is zero-based, so index 1 refers to the second row. The resulting subset_df DataFrame will contain the selected rows.

copy only specific columns from one dataframe to another dataframe

You can copy specific columns from one DataFrame to another DataFrame by using indexing. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data1 = {'A': [1, 2, 3],
         'B': ['a', 'b', 'c'],
         'C': [True, False, True]}

df1 = pd.DataFrame(data1)

# Create an empty DataFrame with desired column names
columns_to_copy = ['A', 'C']
df2 = pd.DataFrame(columns=columns_to_copy)

# Copy specific columns from df1 to df2
for column in columns_to_copy:
    df2[column] = df1[column]

print(df2)

In this example, data1 represents the original DataFrame df1. We want to copy only columns 'A' and 'C' from df1 to a new DataFrame df2. We create an empty DataFrame df2 with the desired column names ('A' and 'C'). Then, we iterate over the columns we want to copy and assign the corresponding columns from df1 to df2. Finally, we print df2, which contains only the specified columns from df1.

apply custom method for multiple columns + pandas

To apply a custom method to multiple columns in a pandas DataFrame, you can use the apply() function along with a lambda function or a custom function. Here's how you can do it:

Suppose you have a custom method called custom_function that you want to apply to multiple columns:

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3],
        'B': [4, 5, 6],
        'C': [7, 8, 9]}

df = pd.DataFrame(data)

# Custom function
def custom_function(x):
    # Example: multiplying each value by 2
    return x * 2

# Apply custom function to multiple columns using apply()
columns_to_apply = ['A', 'B', 'C']
df[columns_to_apply] = df[columns_to_apply].apply(custom_function)

print(df)

In this example, custom_function is a custom method that multiplies each value by 2. We want to apply this method to columns 'A', 'B', and 'C' in the DataFrame df. We specify the columns we want to apply the function to using df[columns_to_apply] and then apply the custom function using the apply() function.

Make sure that your custom function operates correctly on the data type of the columns you're applying it to. In this example, since the data type is integer, the custom function should be compatible with integer operations. Adjust your custom function accordingly based on the operations you want to perform on the columns.

applying-function-with-multiple-arguments-to-create-a-new-pandas-column

To apply a function with multiple arguments to create a new column in a pandas DataFrame, you can use the apply() function along with a lambda function or a custom function. Here's how you can do it:

Suppose you have a custom function called custom_function that takes multiple arguments and you want to apply it to create a new column:

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3],
        'B': [4, 5, 6]}

df = pd.DataFrame(data)

# Custom function with multiple arguments
def custom_function(x, y):
    return x + y

# Apply custom function to create a new column
df['C'] = df.apply(lambda row: custom_function(row['A'], row['B']), axis=1)

print(df)

In this example, custom_function is a custom method that takes two arguments x and y and returns their sum. We want to apply this function to create a new column 'C' in the DataFrame df by adding values from columns 'A' and 'B' for each row. We use the apply() function along with a lambda function to apply the custom function to each row of the DataFrame. The axis=1 parameter specifies that the function should be applied along rows. Finally, the result is assigned to the new column 'C' in the DataFrame.

Adjust your custom function according to the operations you want to perform on the columns and make sure that the function signature matches the arguments you provide when using apply().

rename column in pandas

To rename a column in pandas DataFrame, you can use the rename() function. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'old_column_name': [1, 2, 3],
        'B': ['a', 'b', 'c']}

df = pd.DataFrame(data)

# Rename column
df = df.rename(columns={'old_column_name': 'new_column_name'})

print(df)

In this example, rename() function is used to rename the column 'old_column_name' to 'new_column_name'. You pass a dictionary to the columns parameter where keys are the old column names and values are the new column names you want to assign. Finally, the result is assigned back to the DataFrame or you can use inplace=True parameter to rename the column in place without having to assign back.

pandas column values as a list

To retrieve the values of a pandas DataFrame column as a Python list, you can use the .tolist() method on the specific column. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3],
        'B': ['a', 'b', 'c']}

df = pd.DataFrame(data)

# Get column 'A' values as a list
column_A_list = df['A'].tolist()

print(column_A_list)

This code will print the values of column 'A' as a Python list. You can replace 'A' with the name of any other column to get the values of that column as a list.

creating-an-empty-pandas-data frame-and-then-filling-it

You can create an empty pandas DataFrame and then fill it with data using various methods. Here's how you can do it:

import pandas as pd

# Create an empty DataFrame with column names
columns = ['A', 'B', 'C']
df = pd.DataFrame(columns=columns)

# Fill the DataFrame with data
data = {'A': [1, 2, 3],
        'B': ['a', 'b', 'c'],
        'C': [True, False, True]}

# Method 1: Using .loc[] for each row
for index, row_data in enumerate(data.values()):
    df.loc[index] = row_data

# Method 2: Using .append() method
for row_data in data.values():
    df = df.append(pd.Series(row_data, index=df.columns), ignore_index=True)

print(df)

In this code:

First, an empty DataFrame df with columns 'A', 'B', and 'C' is created.
Then, the data dictionary contains the data you want to fill the DataFrame with.
Two methods are demonstrated for filling the DataFrame:
- Method 1 uses .loc[] to assign each row of data to the DataFrame.
- Method 2 uses the .append() method to append each row of data to the DataFrame.
Finally, the DataFrame df is printed to show the filled data.

Choose the method that suits your preference or requirements for filling the DataFrame.

drop columns pandas

To drop columns from a pandas DataFrame, you can use the drop() function. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3],
        'B': ['a', 'b', 'c'],
        'C': [True, False, True]}

df = pd.DataFrame(data)

# Drop column 'B'
df.drop(columns=['B'], inplace=True)

print(df)

In this example, drop() function is used to drop column 'B' from the DataFrame df. The columns parameter specifies the list of column names to drop. Setting inplace=True applies the operation directly to the DataFrame df without creating a new DataFrame. Finally, the result is printed, showing the DataFrame with the specified column dropped.

dataframe apply with param

You can use the apply() function in pandas DataFrame to apply a function with parameters along either axis of the DataFrame. If you need to apply a function that requires additional parameters, you can use a lambda function to pass those parameters. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3, 4, 5],
        'B': [6, 7, 8, 9, 10]}

df = pd.DataFrame(data)

# Custom function that takes a parameter
def custom_function(row, param):
    return row['A'] + row['B'] + param

# Parameter for the custom function
custom_param = 5

# Apply the custom function with parameter using a lambda function
df['result'] = df.apply(lambda row: custom_function(row, custom_param), axis=1)

print(df)

In this example:

We have a custom function custom_function that takes a DataFrame row and a parameter param as input and returns the sum of columns 'A' and 'B' along with the parameter.
We define a parameter custom_param for the custom function.
We use the apply() function along with a lambda function to apply the custom function to each row of the DataFrame df. The lambda function passes each row along with the parameter to the custom_function.
The result is stored in a new column called 'result' in the DataFrame.

Adjust the custom function and the parameters according to your specific requirements.

call with dynamic arguments python

You can call a function with dynamic arguments in Python by using the *args and **kwargs syntax. *args collects positional arguments into a tuple, and **kwargs collects keyword arguments into a dictionary. Here's how you can do it:

def dynamic_function(*args, **kwargs):
    print("Positional arguments (args):", args)
    print("Keyword arguments (kwargs):", kwargs)

# Example call with dynamic arguments
dynamic_function(1, 2, 3, a='x', b='y', c='z')

This will output:

Positional arguments (args): (1, 2, 3)
Keyword arguments (kwargs): {'a': 'x', 'b': 'y', 'c': 'z'}

In this example, dynamic_function can accept any number of positional and keyword arguments. When you call dynamic_function, you can pass any number of arguments, and they will be collected into the args tuple and kwargs dictionary, respectively.

You can then use these arguments within the function as needed. This allows for flexible function calls with varying numbers of arguments.

return multiple values with dynamic option in apply method of pandas

To return multiple values from a function applied using the apply() method in pandas, you can return a tuple or a DataFrame with multiple columns. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3, 4, 5],
        'B': [6, 7, 8, 9, 10]}

df = pd.DataFrame(data)

# Custom function that returns multiple values
def custom_function(row, param):
    result1 = row['A'] + row['B'] + param
    result2 = row['A'] * row['B'] + param
    return result1, result2

# Parameter for the custom function
custom_param = 5

# Apply the custom function with dynamic arguments using a lambda function
df[['result1', 'result2']] = df.apply(lambda row: custom_function(row, custom_param), axis=1, result_type='expand')

print(df)

This will output:

   A   B  result1  result2
0  1   6       12       11
1  2   7       14       19
2  3   8       16       29
3  4   9       18       41
4  5  10       20       55

In this example, custom_function returns two values result1 and result2. We apply this function to each row of the DataFrame df using the apply() method. The result_type='expand' parameter ensures that the returned tuple is expanded into separate columns in the DataFrame. Finally, we assign the resulting columns to the DataFrame df.

rename column in pandas

To rename a column in pandas DataFrame, you can use the rename() function. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'old_column_name': [1, 2, 3],
        'B': ['a', 'b', 'c']}

df = pd.DataFrame(data)

# Rename column
df.rename(columns={'old_column_name': 'new_column_name'}, inplace=True)

print(df)

In this example, rename() function is used to rename the column 'old_column_name' to 'new_column_name'. You pass a dictionary to the columns parameter where keys are the old column names and values are the new column names you want to assign. Finally, the inplace=True parameter applies the changes directly to the DataFrame df without creating a new DataFrame. If inplace is set to False (or omitted), it returns a new DataFrame with the columns renamed.

keep only specific columns in pandas

To keep only specific columns in a pandas DataFrame, you can use indexing to select only the columns you want to keep. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3],
        'B': ['a', 'b', 'c'],
        'C': [True, False, True]}

df = pd.DataFrame(data)

# Keep only specific columns
columns_to_keep = ['A', 'C']
df = df[columns_to_keep]

print(df)

In this example, columns_to_keep contains the names of the columns you want to keep in the DataFrame. By indexing df with columns_to_keep, only the specified columns are selected, and the resulting DataFrame df contains only those columns. This operation effectively removes any columns not listed in columns_to_keep.

read_csv xlsx pandas

To read both CSV and Excel files into pandas DataFrames, you can use the read_csv() and read_excel() functions, respectively. Here's how you can do it:

import pandas as pd

# Read CSV file into DataFrame
csv_df = pd.read_csv('your_csv_file.csv')

# Read Excel file into DataFrame
xlsx_df = pd.read_excel('your_excel_file.xlsx')

# Display the DataFrames
print("CSV DataFrame:")
print(csv_df)

print("\nExcel DataFrame:")
print(xlsx_df)

Replace 'your_csv_file.csv' and 'your_excel_file.xlsx' with the actual file paths of your CSV and Excel files, respectively.

These functions will read the files into pandas DataFrames, allowing you to manipulate and analyze the data using pandas.

pandas columns as list

If you want to extract the names of the columns of a pandas DataFrame as a list, you can use the columns attribute of the DataFrame. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3],
        'B': ['a', 'b', 'c'],
        'C': [True, False, True]}

df = pd.DataFrame(data)

# Get columns as a list
columns_list = df.columns.tolist()

print(columns_list)

This will print:

['A', 'B', 'C']

In this example, df.columns returns a pandas Index object containing the names of the columns. By calling the tolist() method on this Index object, you can convert it into a regular Python list containing the column names.

append-dictionary-to-data-frame

To append a dictionary to a pandas DataFrame as a new row, you can use the append() method. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3],
        'B': ['a', 'b', 'c'],
        'C': [True, False, True]}

df = pd.DataFrame(data)

# New dictionary to append
new_data = {'A': 4, 'B': 'd', 'C': False}

# Append dictionary to DataFrame
df = df.append(new_data, ignore_index=True)

print(df)

This will append the new dictionary as a new row to the DataFrame df. The ignore_index=True parameter ensures that the index is reset after appending the new row, so the index of the new row will be consecutive.

Please note that for this method to work, the keys of the dictionary should match the column names of the DataFrame, and the values should match the data types of the corresponding columns.

how-to-combine-multiple-columns-in-a-pandas-dataframe-by-using-apply

You can combine multiple columns in a pandas DataFrame using the apply() function along with a custom function that performs the combination. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3],
        'B': ['a', 'b', 'c'],
        'C': ['x', 'y', 'z']}

df = pd.DataFrame(data)

# Custom function to combine columns
def combine_columns(row):
    return '{}-{}'.format(row['B'], row['C'])

# Apply the custom function to combine columns
df['Combined'] = df.apply(combine_columns, axis=1)

print(df)

In this example:

We define a custom function combine_columns(row) that takes a DataFrame row as input and returns the combination of columns 'B' and 'C' separated by a hyphen.
We use the apply() function along with the custom function to apply the combination logic to each row of the DataFrame. The axis=1 parameter specifies that the function should be applied along rows.
The result is stored in a new column called 'Combined' in the DataFrame.

Adjust the custom function according to the combination logic you want to apply to the columns. You can combine any number of columns and perform any type of combination operation inside the custom function.

rename pandas columns

You can rename columns in a pandas DataFrame using the rename() function. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'old_column1': [1, 2, 3],
        'old_column2': ['a', 'b', 'c']}

df = pd.DataFrame(data)

# Rename columns
df.rename(columns={'old_column1': 'new_column1', 'old_column2': 'new_column2'}, inplace=True)

print(df)

In this example, rename() function is used to rename columns 'old_column1' to 'new_column1' and 'old_column2' to 'new_column2'. You pass a dictionary to the columns parameter where keys are the old column names and values are the new column names you want to assign. The inplace=True parameter applies the changes directly to the DataFrame df without creating a new DataFrame. If inplace is set to False (or omitted), it returns a new DataFrame with the columns renamed.

drop pandas columns

To drop columns from a pandas DataFrame, you can use the drop() function. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'A': [1, 2, 3],
        'B': ['a', 'b', 'c'],
        'C': [True, False, True]}

df = pd.DataFrame(data)

# Drop columns 'B' and 'C'
df.drop(columns=['B', 'C'], inplace=True)

print(df)

In this example, drop() function is used to drop columns 'B' and 'C' from the DataFrame df. The columns parameter specifies the list of column names to drop. Setting inplace=True applies the operation directly to the DataFrame df without creating a new DataFrame. If inplace is set to False (or omitted), it returns a new DataFrame with the specified columns dropped.

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