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, [])
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)
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)
The print for average of the spreads come out grouped and calculated right. Why do I get this returned as the result for the std_deviation column instead of the standard deviation of the spread grouped by ticker?:
pandas.core.groupby.SeriesGroupBy object at 0x000000000484A588
df = pd.read_csv('C:\\Users\\William\\Desktop\\tickdata.csv',
dtype={'ticker': str, 'bidPrice': np.float64, 'askPrice': np.float64, 'afterHours': str},
usecols=['ticker', 'bidPrice', 'askPrice', 'afterHours'],
nrows=3000000
)
df = df[df.afterHours == "False"]
df = df[df.bidPrice != 0]
df = df[df.askPrice != 0]
df['spread'] = (df.askPrice - df.bidPrice)
df['std_deviation'] = df['spread'].std(ddof=0)
df = df.groupby(['ticker'])
print(df['std_deviation'])
print(df['spread'].mean())
UPDATE: no longer being returned an object but now trying to figure out how to have the standard deviation displayed by ticker
df['spread'] = (df.askPrice - df.bidPrice)
df2 = df.groupby(['ticker'])
print(df2['spread'].mean())
df = df.set_index('ticker')
print(df['spread'].std(ddof=0))
UPDATE2: got the dataset I needed using
df = df[df.afterHours == "False"]
df = df[df.bidPrice != 0]
df = df[df.askPrice != 0]
df['spread'] = (df.askPrice - df.bidPrice)
print(df.groupby(['ticker'])['spread'].mean())
print(df.groupby(['ticker'])['spread'].std(ddof=0))
This line:
df = df.groupby(['ticker'])
assigns df to a DataFrameGroupBy object, and
df['std_deviation']
is a SeriesGroupBy object (of the column).
It's a good idea not to "shadow" / re-assign one variable to a completely different datatype. Try to use a different variable name for the groupby!
I need help to get the position of the column or another way to read in the column two step left of the column Spannung.
Exceldata = pd.read_excel(str(Dateien[0]), header=[2])
print Dateien[0]
Spannung = Exceldata.columns[Exceldata.columns.str.contains('Spannung effektiv L1')]
print Spannung
IIUC you can use .get_loc
So:
pos = Exceldata.columns.get_loc(Spannung[0])
then you can index left:
other_col = Exceldata.columns[pos -2]
Example:
In [169]:
df = pd.DataFrame(columns=['hello','world','python','pandas','Spannung effektiv L1', 'asdas'])
spannung = df.columns[df.columns.str.contains('Spannung')]
spannung
Out[169]:
Index(['Spannung effektiv L1'], dtype='object')
In [178]:
pos = df.columns.get_loc(spannung[0])
df.columns[pos-2]
Out[178]:
'python'