Pandas Lookup Value In Another Dataframe Top 19 Posts With The Most Views

To lookup a value in another DataFrame using Pandas, you can use the merge() function or the join() function.

Here’s an example of how you can use merge() to lookup a value in another DataFrame:

python
import pandas as pd

# create the first DataFrame
df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'value': [1, 2, 3, 4]})

# create the second DataFrame
df2 = pd.DataFrame({'key': ['B', 'D'], 'lookup_value': ['blue', 'green']})

# merge the two DataFrames on the 'key' column
merged_df = pd.merge(df1, df2, on='key')

# print the merged DataFrame
print(merged_df)

In this example, the merge() function merges df1 and df2 on the 'key' column, creating a new DataFrame called merged_df. The resulting DataFrame contains only the rows from df1 that have a matching value in df2, and includes an additional column called 'lookup_value' that contains the lookup value from df2.

Alternatively, you can use the join() function to achieve the same result:

python
# join the two DataFrames on the 'key' column
joined_df = df1.set_index('key').join(df2.set_index('key'))

# print the joined DataFrame
print(joined_df)

In this example, the join() function joins df1 and df2 on the 'key' column, creating a new DataFrame called joined_df. The resulting DataFrame contains only the rows from df1 that have a matching value in df2, and includes an additional column called 'lookup_value' that contains the lookup value from df2.

To get a column value based on another column in pandas, you can use boolean indexing or the loc function.

Boolean indexing involves using a conditional statement to filter the rows of a dataframe based on a specific column, and then selecting the value of another column for those filtered rows. Here’s an example:

python
import pandas as pd

# create a sample dataframe
data = {'name': ['Alice', 'Bob', 'Charlie', 'Dave'],
        'age': [25, 30, 35, 40],
        'gender': ['F', 'M', 'M', 'M']}
df = pd.DataFrame(data)

# get the age of the person named 'Alice'
alice_age = df.loc[df['name'] == 'Alice', 'age'].values[0]
print(alice_age)  # output: 25

In the example above, we used boolean indexing to filter the rows of the dataframe where the name column is equal to 'Alice'. Then we selected the age column for those filtered rows using the .loc function and retrieved the value using .values[0].

Alternatively, you can also use the loc function to select the value of one column based on the values of another column. Here’s an example:

python
import pandas as pd

# create a sample dataframe
data = {'name': ['Alice', 'Bob', 'Charlie', 'Dave'],
        'age': [25, 30, 35, 40],
        'gender': ['F', 'M', 'M', 'M']}
df = pd.DataFrame(data)

# get the age of the person named 'Bob'
bob_age = df.loc[df['name'] == 'Bob', 'age'].values[0]
print(bob_age)  # output: 30

In this example, we used .loc to select the value of the age column where the name column is equal to 'Bob'. We retrieved the value using .values[0].

To check if a row from one DataFrame is present in another DataFrame, you can use the isin() method of pandas DataFrame.

Here’s an example:

python
import pandas as pd

# create two DataFrames
df1 = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
df2 = pd.DataFrame({'A': [2, 3, 4], 'B': [5, 6, 7]})

# check if each row in df1 is in df2
is_in_df2 = df1.isin(df2.values).all(axis=1)

print(is_in_df2)

Output:

python
0    False
1     True
2    False
dtype: bool

In this example, we first create two DataFrames df1 and df2. We then use the isin() method to check if each row in df1 is present in df2. The isin() method takes the values of df2 as input and returns a DataFrame of the same shape as df1, where each element is a boolean indicating whether the corresponding element in df1 is present in df2.

We then use the all() method along the axis=1 to check if all the elements in each row of df1 are present in df2. The all() method returns a boolean indicating whether all the elements along the specified axis are True.

The result of this operation is a pandas Series object where each element is a boolean indicating whether the corresponding row in df1 is present in df2. In this example, the second row of df1 is present in df2, while the other rows are not.

Pandas provides a powerful feature called “lookup” that allows you to select values from a DataFrame by specifying row and column labels.

Here’s an example of how to use the lookup function in pandas:

python
import pandas as pd

# Create a sample DataFrame
df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]}, index=['X', 'Y', 'Z'])

# Print the DataFrame
print(df)
# Output:
#    A  B  C
# X  1  4  7
# Y  2  5  8
# Z  3  6  9

# Use lookup to select values from the DataFrame
values = df.lookup(['X', 'Y', 'Z'], ['B', 'C', 'A'])

# Print the selected values
print(values)
# Output: [4 8 3]

In this example, we created a DataFrame with three columns (A, B, and C) and three rows (X, Y, and Z). We then used the lookup function to select values from the DataFrame. The first argument to lookup is a list of row labels (in this case, [‘X’, ‘Y’, ‘Z’]), and the second argument is a list of column labels (in this case, [‘B’, ‘C’, ‘A’]). The values returned by lookup are the values from the DataFrame at the specified row and column labels (in this case, the values in column B for row X, the values in column C for row Y, and the values in column A for row Z).

To add a column to a pandas DataFrame based on lookup in another DataFrame, you can use the merge function. Here’s an example:

python
import pandas as pd

# create the first DataFrame
df1 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'value': [1, 2, 3, 4]})

# create the second DataFrame
df2 = pd.DataFrame({'key': ['A', 'B', 'C', 'D'], 'description': ['Apple', 'Banana', 'Cherry', 'Durian']})

# merge the two DataFrames on the 'key' column
merged_df = pd.merge(df1, df2, on='key')

# display the merged DataFrame
print(merged_df)

This will output:

css
  key  value description
0   A      1       Apple
1   B      2      Banana
2   C      3      Cherry
3   D      4      Durian

In this example, we merge df1 and df2 on the 'key' column. The resulting merged_df DataFrame contains columns for both the 'value' column from df1 and the 'description' column from df2. You can then use this merged DataFrame as needed.

To get values from one dataframe based on conditions from another dataframe in Pandas, you can use the merge() method or the join() method. Here’s an example of how to use merge():

Suppose you have two dataframes, df1 and df2. You want to create a new dataframe df3 that contains all the rows from df1 where the value in column A matches a value in column B in df2, and where the value in column C in df2 is greater than 10. Here’s how you can do that:

python
import pandas as pd

# Create dataframe df1
df1 = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [2, 3, 4, 5]})

# Create dataframe df2
df2 = pd.DataFrame({'B': [2, 4, 6], 'C': [5, 15, 25]})

# Merge the two dataframes on column B and filter based on conditions
df3 = pd.merge(df1, df2[df2['C'] > 10], on='B')

print(df3)

Output:

css
   A  B   C
0  1  2  15
1  3  4  15

In this example, we first create two dataframes df1 and df2. Then we merge the two dataframes on column B using pd.merge(). The resulting dataframe df3 contains only the rows where B matches between df1 and df2, and where C in df2 is greater than 10.