pandas notes 2

https://chat.openai.com/c/74c7fcb9-c547-4309-b647-2329b55c99ca

26

get unique values in a specific column in pandas

To get unique values in a specific column in pandas, you can use the unique() function. Here's an example:

import pandas as pd

# Sample DataFrame
data = {'Column1': [1, 2, 3, 4, 5, 1, 2, 3, 4, 5],
        'Column2': ['A', 'B', 'C', 'D', 'E', 'A', 'B', 'C', 'D', 'E']}
df = pd.DataFrame(data)

# Get unique values in 'Column2'
unique_values = df['Column2'].unique()

print(unique_values)

This will output:

['A' 'B' 'C' 'D' 'E']

This gives you an array of unique values present in the specified column of your DataFrame.

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27

replace value on specific column in pandas

To replace values in a specific column in a pandas DataFrame, you can use the replace() function. Here's an example:

import pandas as pd

# Sample DataFrame
data = {'Column1': [1, 2, 3, 4, 5],
        'Column2': ['A', 'B', 'C', 'D', 'E']}
df = pd.DataFrame(data)

# Replace 'A' with 'X' in 'Column2'
df['Column2'] = df['Column2'].replace('A', 'X')

print(df)

This will output:

   Column1 Column2
0        1       X
1        2       B
2        3       C
3        4       D
4        5       E

You can also perform replacements on multiple values by passing a dictionary to the replace() function. For example:

# Replace multiple values in 'Column2'
df['Column2'] = df['Column2'].replace({'A': 'X', 'B': 'Y'})

print(df)

This would replace 'A' with 'X' and 'B' with 'Y' in 'Column2'.

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28

pandas-split-dataframe-by-column-value

To split a pandas DataFrame based on unique values in a specific column, you can use the groupby() function followed by iteration through the resulting groups. Here's an example:

import pandas as pd

# Sample DataFrame
data = {'Category': ['A', 'B', 'A', 'B', 'C'],
        'Value': [1, 2, 3, 4, 5]}
df = pd.DataFrame(data)

# Group DataFrame by 'Category'
grouped = df.groupby('Category')

# Iterate through groups
for category, group in grouped:
    print("Category:", category)
    print(group)
    print()

Output:

Category: A
  Category  Value
0        A      1
2        A      3

Category: B
  Category  Value
1        B      2
3        B      4

Category: C
  Category  Value
4        C      5

In this example, the DataFrame is split into groups based on the unique values in the 'Category' column, and each group is printed separately. You can then perform operations on each group as needed.

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29

how-do-i-create-test-and-train-samples-from-one-dataframe-with-pandas

To create test and train samples from one DataFrame in pandas, you can use the train_test_split() function from the sklearn.model_selection module. Here's an example:

import pandas as pd
from sklearn.model_selection import train_test_split

# Sample DataFrame
data = {'Feature1': [1, 2, 3, 4, 5],
        'Feature2': [6, 7, 8, 9, 10],
        'Target': [0, 1, 0, 1, 0]}  # Assuming a binary classification problem
df = pd.DataFrame(data)

# Split the DataFrame into train and test sets
train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)

# Print the shapes of train and test sets
print("Train set shape:", train_df.shape)
print("Test set shape:", test_df.shape)

In this example:

• test_size=0.2 specifies that 20% of the data will be used for the test set, while the remaining 80% will be used for the training set.

• random_state ensures reproducibility of the results. You can change or remove it if you want different splits each time you run the code.

After splitting, train_df will contain 80% of the original DataFrame's rows, and test_df will contain the remaining 20%. You can then use these DataFrames for training and testing your machine learning models.

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30

tables-in-markdown-in-jupyter

To create tables in Markdown within Jupyter Notebook, you can use Markdown syntax. Here's an example of how to create a simple table:

| Column 1 Header | Column 2 Header | Column 3 Header |
|-----------------|-----------------|-----------------|
| Row 1, Col 1    | Row 1, Col 2    | Row 1, Col 3    |
| Row 2, Col 1    | Row 2, Col 2    | Row 2, Col 3    |
| Row 3, Col 1    | Row 3, Col 2    | Row 3, Col 3    |

This Markdown code will generate a table like this:

Column 1 Header	Column 2 Header	Column 3 Header
Row 1, Col 1	Row 1, Col 2	Row 1, Col 3
Row 2, Col 1	Row 2, Col 2	Row 2, Col 3
Row 3, Col 1	Row 3, Col 2	Row 3, Col 3

You can adjust the number of columns and rows as needed. Additionally, you can use Markdown formatting to make your table more readable, such as adding bold text or specifying alignment. Here's another example:

| **Name** | **Age** | **Gender** |
|----------|---------|------------|
| John     | 30      | Male       |
| Alice    | 25      | Female     |
| Bob      | 40      | Male       |

This Markdown code will generate a formatted table like this:

Name	Age	Gender
John	30	Male
Alice	25	Female
Bob	40	Male

You can use this Markdown syntax directly within Markdown cells in Jupyter Notebook to create tables.

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31

update single row pandas

To update a single row in a pandas DataFrame, you can use the at or loc accessor. Here's an example of how to update a single row:

import pandas as pd

# Sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob'],
        'Age': [30, 25, 40],
        'Gender': ['Male', 'Female', 'Male']}
df = pd.DataFrame(data)

# Update the row with index 1
df.at[1, 'Age'] = 26
df.at[1, 'Gender'] = 'Non-Binary'  # Update multiple columns if needed

print(df)

This will update the row with index 1 in the DataFrame df with the new values for 'Age' and 'Gender'. The output will be:

    Name  Age      Gender
0   John   30        Male
1  Alice   26  Non-Binary
2    Bob   40        Male

Alternatively, you can use loc to achieve the same result:

# Using loc to update the row with index 1
df.loc[1, ['Age', 'Gender']] = [26, 'Non-Binary']

print(df)

The result will be the same as above. Both at and loc are efficient ways to update specific rows and columns in a pandas DataFrame.

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32

pandas-replacing-or-updating-an-entire-row-conditionally

To replace or update an entire row conditionally in a pandas DataFrame, you can use boolean indexing to identify the rows that meet the condition and then assign new values to those rows. Here's an example:

import pandas as pd

# Sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob'],
        'Age': [30, 25, 40],
        'Gender': ['Male', 'Female', 'Male']}
df = pd.DataFrame(data)

# Conditionally update the row where Name is 'Alice'
condition = df['Name'] == 'Alice'

# Update the entire row where the condition is True
df.loc[condition] = ['Alice', 26, 'Non-Binary']  # New values for the entire row

print(df)

This will update the row where the name is 'Alice' with the new values provided. The output will be:

    Name  Age      Gender
0   John   30        Male
1  Alice   26  Non-Binary
2    Bob   40        Male

In this example, condition holds a boolean Series indicating which rows satisfy the condition 'Name' == 'Alice'. Then, using loc, we assign new values to the rows where the condition is True, effectively updating the entire row based on the condition.

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33

update-the-value-of-a-row in pandas

To update the value of a specific row in pandas, you can use the .loc indexer to select the row based on its index and then update the values accordingly. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob'],
        'Age': [30, 25, 40],
        'Gender': ['Male', 'Female', 'Male']}
df = pd.DataFrame(data)

# Update the value of the row with index 1
df.loc[1, 'Age'] = 26  # Updating the 'Age' value of the row with index 1

print(df)

This will update the 'Age' value of the row with index 1 from 25 to 26 in the DataFrame df. The output will be:

    Name  Age  Gender
0   John   30    Male
1  Alice   26  Female
2    Bob   40    Male

You can similarly update other columns in the same row by specifying the column name within the .loc indexer.

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34

get-index-when-looping-through-one-column-of-pandas

To loop through one column of a pandas DataFrame and get the index along with the values of that column, you can use the iterrows() function. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob'],
        'Age': [30, 25, 40],
        'Gender': ['Male', 'Female', 'Male']}
df = pd.DataFrame(data)

# Loop through the 'Name' column and print index and value
for index, value in df['Name'].iteritems():
    print("Index:", index, "Value:", value)

Output:

Index: 0 Value: John
Index: 1 Value: Alice
Index: 2 Value: Bob

In this loop, index represents the index of each row, and value represents the value of the 'Name' column at that index. You can replace 'Name' with any column name from your DataFrame to loop through that specific column.

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35

change cell datatype in pandas

You can change the datatype of a cell in a pandas DataFrame using the astype() function or by directly assigning a new value of a different datatype to that cell. Here are both methods:

1. Using astype():

import pandas as pd

# Sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob'],
        'Age': [30, 25, 40],
        'Gender': ['Male', 'Female', 'Male']}
df = pd.DataFrame(data)

# Change the datatype of a specific cell in the 'Age' column to float
df.at[1, 'Age'] = df.at[1, 'Age'].astype(float)

print(df.dtypes)  # Check the data types of DataFrame after conversion

Output:

Name      object
Age      float64
Gender    object
dtype: object

1. Directly assigning a new value:

# Directly change the datatype of a specific cell in the 'Age' column to float
df.at[1, 'Age'] = float(df.at[1, 'Age'])

print(df.dtypes)  # Check the data types of DataFrame after conversion

Both methods will convert the datatype of the specified cell in the DataFrame. In these examples, the datatype of the cell in the 'Age' column at row index 1 is changed from integer to float.

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36

pandas-dataframe-doesnt-have-the-entire-data-and-uses-three-dots-instead

If your pandas DataFrame doesn't display the entire data and instead shows three dots (...) in Jupyter Notebook or IPython environment, it means that the display is truncated, typically to save space when printing large DataFrames. However, you can change this behavior to display the entire DataFrame by adjusting pandas' display options.

Here's how you can do it:

import pandas as pd

# Set display options to show all rows and columns
pd.set_option('display.max_rows', None)    # Set to None to show all rows
pd.set_option('display.max_columns', None) # Set to None to show all columns

# Your DataFrame here
# df = ...

# Display your DataFrame
# print(df)

By setting display.max_rows and display.max_columns to None, pandas will display all rows and columns of your DataFrame, ensuring that you see the entire dataset without truncation.

Remember to adjust these display options back to their default values after you've finished inspecting your DataFrame, especially if you're working with very large datasets, as displaying all rows and columns can be resource-intensive and may slow down performance. You can set them back to their default values like this:

# Reset display options to default
pd.reset_option('display.max_rows')
pd.reset_option('display.max_columns')

This way, you can easily toggle between displaying all data and the truncated view in your Jupyter Notebook or IPython environment.

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37

whats-the-difference-between-df-head-and-df-head

In pandas, there is no difference between df.head() and df.head. Both df.head() and df.head are used to display the first few rows of a DataFrame. The head() method is a built-in function in pandas that returns the first n rows of a DataFrame, where n is the number of rows specified as an argument (default is 5 if no argument is provided). On the other hand, df.head is a property that refers to the same head() method, and it is provided for convenience.

Here's an example:

import pandas as pd

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

# Using df.head() method
print(df.head())

# Using df.head property
print(df.head)

Both df.head() and df.head will output the same result, which is the first 5 rows of the DataFrame:

   A  B
0  1  a
1  2  b
2  3  c
3  4  d
4  5  e

In summary, both df.head() and df.head are used to display the first few rows of a DataFrame, and they produce the same result.

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38

read xlsm in pandas

To read Excel files with the .xlsm extension in pandas, you can use the pd.read_excel() function. This function can read both .xlsx and .xlsm files. Here's how you can do it:

import pandas as pd

# Read the Excel file
df = pd.read_excel('your_file_name.xlsm')

# Display the DataFrame
print(df)

Replace 'your_file_name.xlsm' with the path to your .xlsm file. If your Excel file contains multiple sheets and you want to read a specific sheet, you can specify it using the sheet_name parameter:

# Read a specific sheet from the Excel file
df = pd.read_excel('your_file_name.xlsm', sheet_name='Sheet1')

# Display the DataFrame
print(df)

You can also use other parameters of pd.read_excel() to customize the reading process according to your requirements, such as specifying specific columns, skipping rows, or parsing dates.

Keep in mind that to read .xlsm files, you should have the necessary dependencies installed, including xlrd for reading .xls files and openpyxl for reading .xlsx and .xlsm files. You can install them using pip:

pip install xlrd openpyxl

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39

how to detect outliers in pandas

Detecting outliers in pandas typically involves using statistical methods to identify data points that are significantly different from the rest of the data. Here are a few common methods to detect outliers:

1. Standard Deviation Method: Identify data points that are more than a certain number of standard deviations away from the mean.

2. Interquartile Range (IQR) Method: Identify data points that fall outside the range defined by the first quartile (Q1) minus 1.5 times the interquartile range (IQR) and the third quartile (Q3) plus 1.5 times the IQR.

3. Z-score Method: Calculate the Z-score for each data point and identify those with a Z-score greater than a specified threshold.

Here's an example of using the standard deviation method and the IQR method to detect outliers in a pandas DataFrame:

import pandas as pd

# Sample DataFrame
data = {'Value': [1, 2, 3, 4, 5, 6, 7, 8, 9, 100]}  # Introducing an outlier with a value of 100
df = pd.DataFrame(data)

# Standard Deviation Method
mean = df['Value'].mean()
std_dev = df['Value'].std()
threshold = 3  # Adjust this threshold as needed
outliers_std = df[(df['Value'] - mean).abs() > threshold * std_dev]

# Interquartile Range (IQR) Method
q1 = df['Value'].quantile(0.25)
q3 = df['Value'].quantile(0.75)
iqr = q3 - q1
outliers_iqr = df[(df['Value'] < q1 - 1.5 * iqr) | (df['Value'] > q3 + 1.5 * iqr)]

print("Outliers using Standard Deviation Method:")
print(outliers_std)
print("\nOutliers using Interquartile Range (IQR) Method:")
print(outliers_iqr)

You can adjust the threshold for each method based on your data and the level of sensitivity you want for detecting outliers. Additionally, you can explore other methods and techniques for outlier detection depending on the characteristics of your data and the specific requirements of your analysis.

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40

lowercase and standardize the columns

before: "Current City", "Old City"

after: "current_city", "old_city"

You can lowercase and standardize the column names in a pandas DataFrame using the str.lower() method. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'Current City': ['New York', 'Los Angeles', 'Chicago'],
        'Old City': ['San Francisco', 'Miami', 'Seattle']}
df = pd.DataFrame(data)

# Lowercase and standardize column names
df.columns = df.columns.str.lower()

print(df)

Output:

  current city       old city
0     New York  San Francisco
1  Los Angeles          Miami
2      Chicago        Seattle

In this example, df.columns.str.lower() converts all column names to lowercase using the str.lower() method. Then, we assign these lowercase column names back to the DataFrame's columns using df.columns = .... This will standardize the column names to lowercase as you specified.

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41

add row to existing dataframe + pandas

To add a row to an existing DataFrame in pandas, you can use the append() method or directly assign a new row to the DataFrame using the .loc indexer. Here's how you can do it:

Using append() method:

import pandas as pd

# Sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob'],
        'Age': [30, 25, 40],
        'Gender': ['Male', 'Female', 'Male']}
df = pd.DataFrame(data)

# New row to add
new_row = {'Name': 'Emma', 'Age': 35, 'Gender': 'Female'}

# Append the new row to the DataFrame
df = df.append(new_row, ignore_index=True)

print(df)

Output:

    Name  Age  Gender
0   John   30    Male
1  Alice   25  Female
2    Bob   40    Male
3   Emma   35  Female

Using .loc indexer:

# New row to add
new_row = {'Name': 'Emma', 'Age': 35, 'Gender': 'Female'}

# Add the new row using .loc indexer
df.loc[len(df)] = new_row

print(df)

Output will be the same as above.

Both methods achieve the same result of adding a new row to the existing DataFrame. You can choose the method that you find more convenient or suitable for your use case.

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42

remove not null on specific column pandas

To remove rows in a pandas DataFrame where a specific column has a not-null value, you can use boolean indexing. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'Name': ['John', 'Alice', None, 'Bob', 'Emma'],
        'Age': [30, 25, None, 40, None],
        'Gender': ['Male', 'Female', None, 'Male', None]}
df = pd.DataFrame(data)

# Remove rows where the 'Age' column is not null
df = df[df['Age'].notnull()]

print(df)

This will remove all rows where the 'Age' column has a not-null value, leaving only the rows where 'Age' is null:

    Name   Age  Gender
0   John  30.0    Male
1  Alice  25.0  Female
3    Bob  40.0    Male

In this example, we used df['Age'].notnull() to create a boolean mask where True represents rows with non-null values in the 'Age' column. Then, we used this mask to select rows where 'Age' is not null using boolean indexing (df[mask]). Finally, we assigned the filtered DataFrame back to df, effectively removing rows with not-null values in the 'Age' column.

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43

using-len-in-pandas-dataframe

In a pandas DataFrame, the len() function returns the number of rows in the DataFrame. You can use it as follows:

import pandas as pd

# Sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob'],
        'Age': [30, 25, 40],
        'Gender': ['Male', 'Female', 'Male']}
df = pd.DataFrame(data)

# Get the number of rows in the DataFrame
num_rows = len(df)

print("Number of rows in the DataFrame:", num_rows)

Output:

Number of rows in the DataFrame: 3

In this example, len(df) returns the number of rows in the DataFrame df, which is 3. This can be useful for various purposes, such as determining the size of your dataset or iterating over rows in a loop.

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45

converting-a-pandas-groupby-multiindex-output-from-series-back-to-dataframe

You can convert a pandas GroupBy object with a multi-index output from a Series back to a DataFrame using the reset_index() method. This method will convert the indices of the Series back into columns. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'A': ['foo', 'bar', 'foo', 'bar', 'foo', 'bar'],
        'B': ['one', 'one', 'two', 'two', 'three', 'three'],
        'C': [1, 2, 3, 4, 5, 6]}
df = pd.DataFrame(data)

# Group by columns 'A' and 'B' and calculate the sum of column 'C'
grouped = df.groupby(['A', 'B'])['C'].sum()

# Convert the Series back to a DataFrame
df_back = grouped.reset_index()

print(df_back)

Output:

     A      B   C
0  bar    one   2
1  bar  three  10
2  bar    two   4
3  foo    one   1
4  foo  three   5
5  foo    two   3

In this example, we first group the DataFrame df by columns 'A' and 'B', and then calculate the sum of column 'C' for each group. The resulting output is a Series with a multi-index. We then use reset_index() to convert this Series back into a DataFrame, where the indices become columns again.

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46

group by specific type in a column

To group a DataFrame by a specific type in a column, you can use boolean indexing to filter the DataFrame first and then perform the grouping operation. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'Category': ['A', 'B', 'A', 'B', 'C'],
        'Value': [1, 2, 3, 4, 5]}
df = pd.DataFrame(data)

# Filter the DataFrame for a specific type in the 'Category' column
specific_type = 'A'
filtered_df = df[df['Category'] == specific_type]

# Group by the filtered DataFrame
grouped = filtered_df.groupby('Category')['Value'].sum()

print(grouped)

Output:

Category
A    4
Name: Value, dtype: int64

In this example, we first filter the DataFrame df to include only rows where the 'Category' column matches the specific type 'A'. Then, we group the filtered DataFrame by the 'Category' column and calculate the sum of the 'Value' column for each group.

You can replace 'A' with any specific type you want to group by in the 'Category' column. This approach allows you to perform group operations on a subset of the DataFrame based on specific criteria.

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47

pandas to csv

To save a pandas DataFrame to a CSV file, you can use the to_csv() method. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob'],
        'Age': [30, 25, 40],
        'Gender': ['Male', 'Female', 'Male']}
df = pd.DataFrame(data)

# Save the DataFrame to a CSV file
df.to_csv('output.csv', index=False)  # Specify index=False to omit the index column in the output file

print("DataFrame successfully saved to 'output.csv'")

This will save the DataFrame df to a CSV file named 'output.csv' in the current directory. The index=False parameter is used to exclude the index column from being written to the CSV file. If you want to include the index, you can omit this parameter or set it to True.

After running the code, you should find a file named 'output.csv' containing the DataFrame data in CSV format in the same directory as your Python script or Jupyter Notebook.

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48

columns in series + pandas

In pandas, a Series is a one-dimensional labeled array capable of holding any data type. If you want to access the columns of a DataFrame as Series, you can use either the indexing ([]) or attribute access (.) method.

Here's how you can access columns as Series:

import pandas as pd

# Sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob'],
        'Age': [30, 25, 40],
        'Gender': ['Male', 'Female', 'Male']}
df = pd.DataFrame(data)

# Access column 'Name' as a Series using indexing method
name_series = df['Name']

# Access column 'Age' as a Series using attribute access method
age_series = df.Age  # This is equivalent to df['Age']

# Access column 'Gender' as a Series using attribute access method
gender_series = df.Gender  # This is equivalent to df['Gender']

print("Name Series:")
print(name_series)
print("\nAge Series:")
print(age_series)
print("\nGender Series:")
print(gender_series)

Output:

Name Series:
0     John
1    Alice
2      Bob
Name: Name, dtype: object

Age Series:
0    30
1    25
2    40
Name: Age, dtype: int64

Gender Series:
0      Male
1    Female
2      Male
Name: Gender, dtype: object

In this example, we access the columns 'Name', 'Age', and 'Gender' of the DataFrame df as Series using both indexing (df['Name']) and attribute access (df.Age and df.Gender). The resulting objects are Series containing the data from the respective columns.

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49

loop series pandas

You can loop through a pandas Series using a for loop, just like you would iterate through a Python list. Here's how you can do it:

import pandas as pd

# Sample Series
s = pd.Series([1, 2, 3, 4, 5])

# Loop through the Series
for value in s:
    print(value)

This will output:

1
2
3
4
5

In this example, each iteration of the for loop prints one value from the Series s. You can perform any operations or calculations you need within the loop for each value in the Series.

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50

get random row in pandas

To get a random row from a pandas DataFrame, you can use the sample() method with the n parameter set to 1. Here's how you can do it:

import pandas as pd

# Sample DataFrame
data = {'Name': ['John', 'Alice', 'Bob', 'Emma', 'Mike'],
        'Age': [30, 25, 40, 35, 28],
        'Gender': ['Male', 'Female', 'Male', 'Female', 'Male']}
df = pd.DataFrame(data)

# Get a random row from the DataFrame
random_row = df.sample(n=1)

print(random_row)

This will output a random row from the DataFrame df. The sample() method with n=1 randomly selects 1 row from the DataFrame.

If you want to get multiple random rows, you can adjust the value of the n parameter accordingly. For example, df.sample(n=3) will get 3 random rows from the DataFrame.

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