I am new to Pandas and would like to learn how to change alternate column names -
I have a dataframe which looks like this -
I would like to change the column names to be like this -
Any suggestion which apply a for loop would be helpful as I have 57 column names to change in the desired pattern.
A little verbose, but it works:
import pandas as pd
df = pd.DataFrame(columns = ['Time', 'Jolt1', 'Jolt2', 'Time', 'Jolt1', 'Jolt2', 'Time', 'Jolt1', 'Jolt2'])
c = 1
length = int(len(df.columns)/3)
my_list = []
for i in range(length):
my_list.append(['Time', 'Jolt'+str(c), 'Jolt'+str(c + 1)])
c = c + 1
df.columns = sum(my_list, [])
Can somebody explain how this works?
df.columns = list(map(str, df.columns))
Your code is not the best way to convert column names to string, use instead:
df.columns = df.columns.astype(str)
Your code:
df.columns = list(map(str, df.columns))
is equivalent to:
df.columns = [str(col) for col in df.columns]
map: for each item in df.columns (iterable), apply the function str on it but the map function returns an iterator, so you need to explicitly execute list to generate the list.
Here are my data:
df1 = pd.read_csv('gp_data.csv',sep= ";")
df2 = pd.read_csv('ms_data.csv',sep= ";")
#2.7.2
student_data = pd.concat([df1, df2])
#2.7.3
df3 = pd.read_csv('gp_grades.csv',sep= ";")
df4 = pd.read_csv('ms_grades.csv',sep= ";")
#2.7.4
student_grades = pd.concat([df3, df4])
#2.7.5
student = pd.merge(left=student_grades, right=student_data, left_on='student_id', right_on='student_id')
Q:Based on the address and guardian, do the following:
Define a function and use agg to apply the function to the traveltime column.
Find out the sum of the travel time for each group.
Do not use Pandas provide function for the answer, but you may use it to check.
Here are my codes below:
#1
def travel(series):
return sum(series == True)
print(student.groupby(['address','guardian']).agg({'traveltime' : travel}))
#2
print(student.agg({'traveltime':'sum'}))
In fact I don't know what kind of function it may need and not so sure whether the second question is satisfied. (print(student.agg({'traveltime':'sum'})))
I can do the following if I want to extract rows whose column "A" contains the substring "hello".
df[df['A'].str.contains("hello")]
How can I select rows whose column is the substring for another word? e.g.
df["hello".contains(df['A'].str)]
Here's an example dataframe
df = pd.DataFrame.from_dict({"A":["hel"]})
df["hello".contains(df['A'].str)]
IIUC, you could apply str.find:
import pandas as pd
df = pd.DataFrame(['hell', 'world', 'hello'], columns=['A'])
res = df[df['A'].apply("hello".find).ne(-1)]
print(res)
Output
A
0 hell
2 hello
As an alternative use __contains__
res = df[df['A'].apply("hello".__contains__)]
print(res)
Output
A
0 hell
2 hello
Or simply:
res = df[df['A'].apply(lambda x: x in "hello")]
print(res)
I have a dataframe with column names, and I want to find the one that contains a certain string, but does not exactly match it. I'm searching for 'spike' in column names like 'spike-2', 'hey spike', 'spiked-in' (the 'spike' part is always continuous).
I want the column name to be returned as a string or a variable, so I access the column later with df['name'] or df[name] as normal. I've tried to find ways to do this, to no avail. Any tips?
Just iterate over DataFrame.columns, now this is an example in which you will end up with a list of column names that match:
import pandas as pd
data = {'spike-2': [1,2,3], 'hey spke': [4,5,6], 'spiked-in': [7,8,9], 'no': [10,11,12]}
df = pd.DataFrame(data)
spike_cols = [col for col in df.columns if 'spike' in col]
print(list(df.columns))
print(spike_cols)
Output:
['hey spke', 'no', 'spike-2', 'spiked-in']
['spike-2', 'spiked-in']
Explanation:
df.columns returns a list of column names
[col for col in df.columns if 'spike' in col] iterates over the list df.columns with the variable col and adds it to the resulting list if col contains 'spike'. This syntax is list comprehension.
If you only want the resulting data set with the columns that match you can do this:
df2 = df.filter(regex='spike')
print(df2)
Output:
spike-2 spiked-in
0 1 7
1 2 8
2 3 9
This answer uses the DataFrame.filter method to do this without list comprehension:
import pandas as pd
data = {'spike-2': [1,2,3], 'hey spke': [4,5,6]}
df = pd.DataFrame(data)
print(df.filter(like='spike').columns)
Will output just 'spike-2'. You can also use regex, as some people suggested in comments above:
print(df.filter(regex='spike|spke').columns)
Will output both columns: ['spike-2', 'hey spke']
You can also use df.columns[df.columns.str.contains(pat = 'spike')]
data = {'spike-2': [1,2,3], 'hey spke': [4,5,6], 'spiked-in': [7,8,9], 'no': [10,11,12]}
df = pd.DataFrame(data)
colNames = df.columns[df.columns.str.contains(pat = 'spike')]
print(colNames)
This will output the column names: 'spike-2', 'spiked-in'
More about pandas.Series.str.contains.
# select columns containing 'spike'
df.filter(like='spike', axis=1)
You can also select by name, regular expression. Refer to: pandas.DataFrame.filter
df.loc[:,df.columns.str.contains("spike")]
Another solution that returns a subset of the df with the desired columns:
df[df.columns[df.columns.str.contains("spike|spke")]]
You also can use this code:
spike_cols =[x for x in df.columns[df.columns.str.contains('spike')]]
Getting name and subsetting based on Start, Contains, and Ends:
# from: https://stackoverflow.com/questions/21285380/find-column-whose-name-contains-a-specific-string
# from: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.str.contains.html
# from: https://cmdlinetips.com/2019/04/how-to-select-columns-using-prefix-suffix-of-column-names-in-pandas/
# from: https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.filter.html
import pandas as pd
data = {'spike_starts': [1,2,3], 'ends_spike_starts': [4,5,6], 'ends_spike': [7,8,9], 'not': [10,11,12]}
df = pd.DataFrame(data)
print("\n")
print("----------------------------------------")
colNames_contains = df.columns[df.columns.str.contains(pat = 'spike')].tolist()
print("Contains")
print(colNames_contains)
print("\n")
print("----------------------------------------")
colNames_starts = df.columns[df.columns.str.contains(pat = '^spike')].tolist()
print("Starts")
print(colNames_starts)
print("\n")
print("----------------------------------------")
colNames_ends = df.columns[df.columns.str.contains(pat = 'spike$')].tolist()
print("Ends")
print(colNames_ends)
print("\n")
print("----------------------------------------")
df_subset_start = df.filter(regex='^spike',axis=1)
print("Starts")
print(df_subset_start)
print("\n")
print("----------------------------------------")
df_subset_contains = df.filter(regex='spike',axis=1)
print("Contains")
print(df_subset_contains)
print("\n")
print("----------------------------------------")
df_subset_ends = df.filter(regex='spike$',axis=1)
print("Ends")
print(df_subset_ends)