One solution would be to do an inner-join on chromosome, exclude the violating rows, and then do left-join with position:
>>> df = pd.merge(position, region, on = 'chromosome', how = 'inner') >>> idx = (df['BP'] < df['start']) | (df['end'] < df['BP']) # violating rows >>> pd.merge(position, df[~idx], on = ['BP', 'chromosome'], how = 'left') BP chromosome end start 0 1500 1 2000 1000 1 1100 2 2000 1000 2 10000 1 NaN NaN 3 2200 3 NaN NaN 4 3300 2 4000 3000 5 400 1 NaN NaN 6 5000 1 5000 4000
How to create multiple pandas dataframe columns based on multiple lines in a cell, across every rows?,Most efficient way to merge multiple rows of a pandas dataframe in to one row, adding new columns to the row, based on values in the initial rows?,How to create a pandas dataframe that contains ordered lists based on analysis conditions applied on multiple columns,How to create multiple columns in Pandas dataframe based on year
One solution would be to do an inner-join on chromosome, exclude the violating rows, and then do left-join with position:
>>> df = pd.merge(position, region, on = 'chromosome', how = 'inner') >>> idx = (df['BP'] < df['start']) | (df['end'] < df['BP']) # violating rows >>> pd.merge(position, df[~idx], on = ['BP', 'chromosome'], how = 'left') BP chromosome end start 0 1500 1 2000 1000 1 1100 2 2000 1000 2 10000 1 NaN NaN 3 2200 3 NaN NaN 4 3300 2 4000 3000 5 400 1 NaN NaN 6 5000 1 5000 4000
The pandas merge() function is used to do database-style joins on dataframes. To merge dataframes on multiple columns, pass the columns to merge on as a list to the on parameter of the merge() function. The following is the syntax:,Note that, the list of columns passed must be present in both the dataframes. If the column names are different in the two dataframes, use the left_on and right_on parameters to pass your column lists to merge on.,Let’s look at an example of using the merge() function to join dataframes on multiple columns. First, let’s create two dataframes that we’ll be joining together. ,For a complete list of pandas merge() function parameters, refer to its documentation.
The pandas merge() function is used to do database-style joins on dataframes. To merge dataframes on multiple columns, pass the columns to merge on as a list to the on parameter of the merge() function. The following is the syntax:
df_merged = pd.merge(df_left, df_right, on = ['Col1', 'Col2', ...], how = 'inner')Let’s look at an example of using the merge() function to join dataframes on multiple columns. First, let’s create two dataframes that we’ll be joining together.
import pandas as pd
# monthly users
df_users = pd.DataFrame({
'Year': [2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2019, 2020, 2020, 2020],
'Quarter': ['Q1', 'Q1', 'Q1', 'Q2', 'Q2', 'Q2', 'Q3', 'Q3', 'Q3', 'Q4', 'Q4', 'Q4', 'Q1', 'Q1', 'Q1'],
'Month': ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec', 'Jan', 'Feb', 'Mar'],
'Users': [150, 170, 160, 200, 190, 196, 210, 225, 260, 210, 212, 219, 630, 598, 321]
})
# advertising partners
df_ad_partners = pd.DataFrame({
'Year': [2019, 2019, 2019, 2019, 2020],
'Quarter': ['Q1', 'Q2', 'Q3', 'Q4', 'Q1']
})If we want to include the advertising partner info alongside the users dataframe, we’ll have to merge the dataframes using a left join on columns “Year” and “Quarter” since the advertising partner information is unique at the “Year” and “Quarter” level.
df_merged = pd.merge(df_users, df_ad_partners, on = ['Year', 'Quarter'], how = 'left')To combine this information into a single DataFrame, we can use the pd.merge() function:,Finally, you may end up in a case where your two input DataFrames have conflicting column names. Consider this example:,We've already seen the default behavior of pd.merge(): it looks for one or more matching column names between the two inputs, and uses this as the key. However, often the column names will not match so nicely, and pd.merge() provides a variety of options for handling this.,The result has a redundant column that we can drop if desired–for example, by using the drop() method of DataFrames:
import pandas as pd
import numpy as np
class display(object):
"""Display HTML representation of multiple objects"""
template = """<div style="float: left; padding: 10px;">
<p style='font-family:"Courier New", Courier, monospace'>{0}</p>{1}
</div>"""
def __init__(self, *args):
self.args = args
def _repr_html_(self):
return '\n'.join(self.template.format(a, eval(a)._repr_html_())
for a in self.args)
def __repr__(self):
return '\n\n'.join(a + '\n' + repr(eval(a))
for a in self.args)
df1 = pd.DataFrame({
'employee': ['Bob', 'Jake', 'Lisa', 'Sue'],
'group': ['Accounting', 'Engineering', 'Engineering', 'HR']
})
df2 = pd.DataFrame({
'employee': ['Lisa', 'Bob', 'Jake', 'Sue'],
'hire_date': [2004, 2008, 2012, 2014]
})
display('df1', 'df2')df3 = pd.merge(df1, df2) df3
df4 = pd.DataFrame({
'group': ['Accounting', 'Engineering', 'HR'],
'supervisor': ['Carly', 'Guido', 'Steve']
})
display('df3', 'df4', 'pd.merge(df3, df4)')df5 = pd.DataFrame({
'group': ['Accounting', 'Accounting',
'Engineering', 'Engineering', 'HR', 'HR'
],
'skills': ['math', 'spreadsheets', 'coding', 'linux',
'spreadsheets', 'organization'
]
})
display('df1', 'df5', "pd.merge(df1, df5)")display('df1', 'df2', "pd.merge(df1, df2, on='employee')")1 week ago Oct 29, 2021 · Let’s merge the two data frames with different columns. It is possible to join the different columns is using concat () method. Syntax: pandas.concat (objs: Union [Iterable [‘DataFrame’], Mapping [Label, ‘DataFrame’]], axis=’0′, join: str = “‘outer'”) DataFrame: It is dataframe name. axis: 0 refers to the row axis and1 ... , 1 day ago Aug 27, 2020 · Fortunately this is easy to do using the pandas merge() function, which uses the following syntax: pd. merge (df1, df2, left_on=['col1','col2'], right_on = ['col1','col2']) This tutorial explains how to use this function in practice. Example 1: Merge on Multiple Columns with Different Names. Suppose we have the following two pandas DataFrames: ,What is the best way to merge these two dataframe such that it appends additional columns to position with the region it falls in if it falls in any region. Giving in this case roughly the following output:,One approach is to write a function to compute the relationship I want and then to use the DataFrame.apply method as follows:
region = pd.DataFrame({
'chromosome': [1, 1, 1, 1, 2, 2, 2, 2],
'start': [1000, 2000, 3000, 4000, 1000, 2000, 3000, 4000],
'end': [2000, 3000, 4000, 5000, 2000, 3000, 4000, 5000]
}) position = pd.DataFrame({
'chromosome': [1, 2, 1, 3, 2, 1, 1],
'BP': [1500, 1100, 10000, 2200, 3300, 400, 5000]
}) print region print position chromosome end start 0 1 2000 1000 1 1 3000 2000 2 1 4000 3000 3 1 5000 4000 4 2 2000 1000 5 2 3000 2000 6 2 4000 3000 7 2 5000 4000 BP chromosome 0 1500 1 1 1100 2 2 10000 1 3 2200 3 4 3300 2 5 400 1 6 5000 1region = pd.DataFrame({
'chromosome': [1, 1, 1, 1, 2, 2, 2, 2],
'start': [1000, 2000, 3000, 4000, 1000, 2000, 3000, 4000],
'end': [2000, 3000, 4000, 5000, 2000, 3000, 4000, 5000]
}) position = pd.DataFrame({
'chromosome': [1, 2, 1, 3, 2, 1, 1],
'BP': [1500, 1100, 10000, 2200, 3300, 400, 5000]
}) print region print positionchromosome end start 0 1 2000 1000 1 1 3000 2000 2 1 4000 3000 3 1 5000 4000 4 2 2000 1000 5 2 3000 2000 6 2 4000 3000 7 2 5000 4000 BP chromosome 0 1500 1 1 1100 2 2 10000 1 3 2200 3 4 3300 2 5 400 1 6 5000 1position['BP'] >= region['start'] & position['BP'] <= region['end'] & position['chromosome'] == region['chromosome']BP chromosome start end 0 1500 1 1000 2000 1 1100 2 1000 2000 2 10000 1 NANA 3 2200 3 NANA 4 3300 2 3000 4000 5 400 1 NANA 6 5000 1 4000 5000
def within(pos, regs): istrue = (pos.loc['chromosome'] == regs['chromosome']) & (pos.loc['BP'] >= regs['start']) & (pos.loc['BP'] <= regs['end']) if istrue.any(): ind = regs.index[istrue].values[0]
return (regs.loc[ind, ['start', 'end']])
else: return (pd.Series([None, None], index = ['start', 'end'])) position[['start', 'end']] = position.apply(lambda x: within(x, region), axis = 1) print position BP chromosome start end 0 1500 1 1000 2000 1 1100 2 1000 2000 2 10000 1 NaN NaN 3 2200 3 NaN NaN 4 3300 2 3000 4000 5 400 1 NaN NaN 6 5000 1 4000 5000>>>df = pd.merge(position, region, on='chromosome', how='inner') >>>idx = (df['BP'] <df['start']) | (df['end'] <df['BP']) # violating rows>>>pd.merge(position, df[~idx], on=['BP', 'chromosome'], how='left')BP chromosome end start 0 1500 1 2000 1000 1 1100 2 2000 1000 2 10000 1 NaN NaN 3 2200 3 NaN NaN 4 3300 2 4000 3000 5 400 1 NaN NaN 6 5000 1 5000 4000
The concat() function performs concatenation operations of multiple tables along one of the axis (row-wise or column-wise).,More options on table concatenation (row and column wise) and how concat can be used to define the logic (union or intersection) of the indexes on the other axes is provided at the section on object concatenation.,Multiple tables can be concatenated both column-wise and row-wise using the concat function.,By default concatenation is along axis 0, so the resulting table combines the rows of the input tables. Let’s check the shape of the original and the concatenated tables to verify the operation:
In[1]: import pandas as pdIn[2]: air_quality_no2 = pd.read_csv("data/air_quality_no2_long.csv",
...: parse_dates = True)
...:
In[3]: air_quality_no2 = air_quality_no2[["date.utc", "location",
...: "parameter", "value"
]]
...:
In[4]: air_quality_no2.head()
Out[4]:
date.utc location parameter value
0 2019 - 06 - 21 00: 00: 00 + 00: 00 FR04014 no2 20.0
1 2019 - 06 - 20 23: 00: 00 + 00: 00 FR04014 no2 21.8
2 2019 - 06 - 20 22: 00: 00 + 00: 00 FR04014 no2 26.5
3 2019 - 06 - 20 21: 00: 00 + 00: 00 FR04014 no2 24.9
4 2019 - 06 - 20 20: 00: 00 + 00: 00 FR04014 no2 21.4In[5]: air_quality_pm25 = pd.read_csv("data/air_quality_pm25_long.csv",
...: parse_dates = True)
...:
In[6]: air_quality_pm25 = air_quality_pm25[["date.utc", "location",
...: "parameter", "value"
]]
...:
In[7]: air_quality_pm25.head()
Out[7]:
date.utc location parameter value
0 2019 - 06 - 18 06: 00: 00 + 00: 00 BETR801 pm25 18.0
1 2019 - 06 - 17 08: 00: 00 + 00: 00 BETR801 pm25 6.5
2 2019 - 06 - 17 07: 00: 00 + 00: 00 BETR801 pm25 18.5
3 2019 - 06 - 17 06: 00: 00 + 00: 00 BETR801 pm25 16.0
4 2019 - 06 - 17 05: 00: 00 + 00: 00 BETR801 pm25 7.5In[8]: air_quality = pd.concat([air_quality_pm25, air_quality_no2], axis = 0)
In[9]: air_quality.head()
Out[9]:
date.utc location parameter value
0 2019 - 06 - 18 06: 00: 00 + 00: 00 BETR801 pm25 18.0
1 2019 - 06 - 17 08: 00: 00 + 00: 00 BETR801 pm25 6.5
2 2019 - 06 - 17 07: 00: 00 + 00: 00 BETR801 pm25 18.5
3 2019 - 06 - 17 06: 00: 00 + 00: 00 BETR801 pm25 16.0
4 2019 - 06 - 17 05: 00: 00 + 00: 00 BETR801 pm25 7.5In[10]: print('Shape of the ``air_quality_pm25`` table: ', air_quality_pm25.shape)
Shape of the ``
air_quality_pm25``
table: (1110, 4)
In[11]: print('Shape of the ``air_quality_no2`` table: ', air_quality_no2.shape)
Shape of the ``
air_quality_no2``
table: (2068, 4)
In[12]: print('Shape of the resulting ``air_quality`` table: ', air_quality.shape)
Shape of the resulting ``
air_quality``
table: (3178, 4)In[13]: air_quality = air_quality.sort_values("date.utc")
In[14]: air_quality.head()
Out[14]:
date.utc location parameter value
2067 2019 - 05 - 07 01: 00: 00 + 00: 00 London Westminster no2 23.0
1003 2019 - 05 - 07 01: 00: 00 + 00: 00 FR04014 no2 25.0
100 2019 - 05 - 07 01: 00: 00 + 00: 00 BETR801 pm25 12.5
1098 2019 - 05 - 07 01: 00: 00 + 00: 00 BETR801 no2 50.5
1109 2019 - 05 - 07 01: 00: 00 + 00: 00 London Westminster pm25 8.0