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make a shift by index with a pandas dataframe


make a shift by index with a pandas dataframe

By : amih3
Date : November 22 2020, 02:42 PM
Does that help the answer was to resample so I won't have any hole, and then apply the answer for this question : How do you shift Pandas DataFrame with a multiindex?
code :


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Python pandas shift dataframe with time index value

Python pandas shift dataframe with time index value


By : Maxim
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , First you want to shift your index by the desired amount, and then reindex, to make things easier I take a copy here, shift the index, and we reindex on the union of the shifted index and the original index to introduce NaN rows:
code :
In [232]:
df1 = df.copy()
df1.index -= 80000000
df1.reindex(df1.index.union(df.index))

Out[232]:
                      acc
index                    
1480681219960000000   8.0
1480681220000000000   9.0
1480681220040000000   7.0
1480681220080000000  10.0
1480681220120000000   6.0
1480681220160000000   NaN
1480681220200000000   NaN
When using df.drop in pandas make the index of rows shift

When using df.drop in pandas make the index of rows shift


By : Lars
Date : March 29 2020, 07:55 AM
With these it helps I am working on a program where I take a random value find coordinates that are similar in distance to them add them to a group and remove them from the Dataframe. , Try to reset index
df = df.reset_index(drop=True)
Create a column that is a shift of the index in a Pandas DataFrame

Create a column that is a shift of the index in a Pandas DataFrame


By : user3087107
Date : March 29 2020, 07:55 AM
may help you . Use Index.to_series, because shift is not implemented for some Index like RangeIndex yet:
code :
df["index_shifted"] = df.index.to_series().shift(-1)
Pandas DataFrame.Shift returns incorrect result when shift by column axis

Pandas DataFrame.Shift returns incorrect result when shift by column axis


By : user3577406
Date : March 29 2020, 07:55 AM
will help you You are right, it is bug, problem is DataFrame.shift with axis=1 shifts object columns to the next column with same dtype.
In sample columns A and D are filled by integers so A is moved to D, columns B and C are filled by floats, so B is moved to C and similar in boolean D1 and E columns.
code :
df3 = df2.astype(object).shift(1, axis=1).infer_objects()
print (df3)
                      A  B     C     D  D1      E
2019-01-01 00:00:00 NaN  1  10.0  11.0 -11  False
2019-01-01 12:00:00 NaN  2  20.0  22.0 -24  False
2019-01-02 00:00:00 NaN  3   NaN  33.0 -51  False
2019-01-02 12:00:00 NaN  4  40.0   NaN -36  False
2019-01-03 00:00:00 NaN  5  50.0  55.0  -2  False

print (df3.dtypes)
A     float64
B       int64
C     float64
D     float64
D1      int64
E        bool
dtype: object
Make Pandas Dataframe column equal to value in another Dataframe based on index

Make Pandas Dataframe column equal to value in another Dataframe based on index


By : Prateek Rajdev
Date : March 29 2020, 07:55 AM
Any of those help I think you first need to rename your columns to align the DataFrames in concat and then reindex to filter by df1.index and df1.columns:
code :
df21 = df2.rename(columns={'given_name':'first_name'})
df31 = df3.rename(columns={'family_name':'surname'})
df = pd.concat([df21, df31]).reindex(index=df1.index, columns=df1.columns)
print (df)
     first_name  surname state
d                             
1          John    Woods    NY
88          Tom   Murphy    CA
190        Dave    Casey    KY
2509       Mike  Johnson    KY
df4 = pd.concat([df21, df31])
df = df4.reindex(index=df1.index.intersection(df4.index), columns=df1.columns)
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