for every city , I want to create a new column which is minmax scalar of another columns (age).
I tried this an get Input contains infinity or a value too large for dtype('float64').
cols=['age']
def f(x):
scaler1=preprocessing.MinMaxScaler()
x[['age_minmax']] = scaler1.fit_transform(x[cols])
return x
df = df.groupby(['city']).apply(f)
From the comments:
df['age'].replace([np.inf, -np.inf], np.nan, inplace=True)
Or
df['age'] = df['age'].replace([np.inf, -np.inf], np.nan)
Related
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 created a functions that returns a dataframe.Now, i want merge all dataframe into one. First, i called all the function and used reduce and merge function.It did not work as expected.The error i am getting is "cannot combine function.It should be dataframe or series.I checked the type of my df,it is dataframe not functions. I don't know where the error is coming from.
def func1():
return df1
def func2():
return df2
def func3():
return df3
def func4():
return df4
def alldfs():
df_1 = func1()
df_2 = func2()
df_3 = func3()
df_4 = func4()
result = reduce(lambda df_1,d_2,df_3,df_4: pd.merge(df_1,df_2,df_3,df_4,on ="EMP_ID"),[df1,df2,df3,df4)
print(result)
You could try something like this ( assuming that EMP_ID is common across all dataframes and you want the intersection of all dataframes ) -
result = pd.merge(df1, df2, on='EMP_ID').merge(df3, on='EMP_ID').merge(df4, on='EMP_ID')
Let's say a given dataframe df contains two date type columns start_date and end_date, they both need to be manipulated with the code below:
df['date'] = df['date'].str.split('d').str[0].add('d')
df['date'] = df['date'].str.replace('Y', '-').str.replace('m', '-').str.replace('d', '')
df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d', errors='coerce').dt.date
Just wonder how I can convert it to a function date_manipulate like this:
def date_manipulate(x):
return ...
Then apply it to those two columns, thanks for your help.
df[['start_date', 'end_date']] = df[['start_date', 'end_date']].apply(date_manipulate)
Change df['date'] for x, because DataFrame.apply processing both columns like Series:
def date_manipulate(x):
x = x.str.split('d').str[0].add('d')
x = x.str.replace('Y', '-').str.replace('m', '-').str.replace('d', '')
x = pd.to_datetime(x, format='%Y-%m-%d', errors='coerce').dt.date
return x
Also is possible simplify code:
def date_manipulate(x):
x = x.str.split('d').str[0].add('d')
x = pd.to_datetime(x, format='%YY%mm%dd', errors='coerce').dt.date
return x
I'm trying to create a function to filter a dataframe from a list of tuples. I've created the below function but it doesn't seem to be working.
The list of tuples would be have dataframe column name, and a min value and a max value to filter.
eg:
eg_tuple = [('colname1', 10, 20), ('colname2', 30, 40), ('colname3', 50, 60)]
My attempted function is below:
def col_cut(df, cutoffs):
for c in cutoffs:
df_filter = df[ (df[c[0]] >= c[1]) & (df[c[0]] <= c[2])]
return df_filter
Note that the function should not filter on rows where the value is equal to max or min. Appreciate the help.
The problem is that you each time take df as the source to filter. You should filter with:
def col_cut(df, cutoffs):
df_filter = df
for col, mn, mx in cutoffs:
dfcol = df_filter[col]
df_filter = df_filter[(dfcol >= mn) & (dfcol <= mx)]
return df_filter
Note that you can use .between(..) [pandas-doc] here:
def col_cut(df, cutoffs):
df_filter = df
for col, mn, mx in cutoffs:
df_filter = df_filter[df_filter[col].between(mn, mx)]
return df_filter
Use np.logical_and + reduce of all masks created by list comprehension with Series.between:
def col_cut(df, cutoffs):
mask = np.logical_and.reduce([df[col].between(min1,max1) for col,min1,max1 in cutoffs])
return df[mask]
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)