Ways to create a Dataframe

# Libraries import pandas as pd

# Dataframe created from a dictionary data1 = { "col1":[1,2,3,4,5,6,7,8,9,10], "col2":[11,12,13,14,15,16,17,18,19,20] } df = pd.DataFrame(data = data1) print(df)

   col1  col2
0     1    11
1     2    12
2     3    13
3     4    14
4     5    15
5     6    16
6     7    17
7     8    18
8     9    19
9    10    20

# Dataframe created from a list of lists data2 = [ [1,2,3,4,5], [6,7,8,9,10], [11,12,13,14,15], [16,17,18,19,20] ] df = pd.DataFrame(data = data2, columns=("col1","col2","col3","col4","col5")) print(df)

   col1  col2  col3  col4  col5
0     1     2     3     4     5
1     6     7     8     9    10
2    11    12    13    14    15
3    16    17    18    19    20

# working from the created dictionary df = pd.DataFrame(data = data1, index=["row1","row2","row3","row4","row5","row6","row7","row8","row9","row10"]) print(df)

       col1  col2
row1      1    11
row2      2    12
row3      3    13
row4      4    14
row5      5    15
row6      6    16
row7      7    17
row8      8    18
row9      9    19
row10    10    20

data4 = [{"col1": 1, "col2": 2}, {"col1": 2, "col2": 4}, {"col1": 3, "col2": 6}, {"col1": 4, "col2": 8}, {"col1": 5, "col2": 10}] df = pd.DataFrame(data = data4) print(df)

   col1  col2
0     1     2
1     2     4
2     3     6
3     4     8
4     5    10

# original lists x = [1, 2, 3, 4, 5] y = [2, 4, 6, 8, 10] # transformed list with zip function data5 = list(zip(x, y)) print(data5) # the dataset created df = pd.DataFrame(data5, columns = ["x", "y"]) print(df)

[(1, 2), (2, 4), (3, 6), (4, 8), (5, 10)]
   x   y
0  1   2
1  2   4
2  3   6
3  4   8
4  5  10

# The 'orient' parameter changes the orientation of columns by rows data6 = {"key1": [1, 4, 7], "key2": [2, 5, 8], "key3": [3, 6, 9]} df = pd.DataFrame.from_dict(data6, orient = "index", columns = ["A", "B", "C"]) print(df)

      A  B  C
key1  1  4  7
key2  2  5  8
key3  3  6  9

# Dataframe created from a dictionary data3 = { "col1":[1,2,3,4,5,6,7,8,9,10], "col2":[11,12,13,14,15,16,17,18,19,20], "col3":[21,22,23,24,25,26,27,28,29,30], "col4":[31,32,33,34,35,36,37,38,39,40], "col5":[41,42,43,44,45,46,47,48,49,50], "col6":[51,52,53,54,55,56,57,58,59,60] } df = pd.DataFrame(data = data3) print(df)

   col1  col2  col3  col4  col5  col6
0     1    11    21    31    41    51
1     2    12    22    32    42    52
2     3    13    23    33    43    53
3     4    14    24    34    44    54
4     5    15    25    35    45    55
5     6    16    26    36    46    56
6     7    17    27    37    47    57
7     8    18    28    38    48    58
8     9    19    29    39    49    59
9    10    20    30    40    50    60

df1 = pd.DataFrame(data = data3, columns=["col1","col4","col6"]) print(df1)

   col1  col4  col6
0     1    31    51
1     2    32    52
2     3    33    53
3     4    34    54
4     5    35    55
5     6    36    56
6     7    37    57
7     8    38    58
8     9    39    59
9    10    40    60

#df["col2"] <-- 1 column df[["col2", "col3"]] #<-- more than one column

col2int64

11 - 20

col3int64

21 - 30

df[df.columns[1]] #<-- 1 column #df[df.columns[[1,2]]] #<-- more than one column #df[df.columns[0:4]] #<-- from column 0 to column 3

# all rows and ... df.loc[:, "col2"] #<-- 1 column #df.loc[:, ["col2","col3"]] #<-- more than one column #df.loc[:, "col2":"col5"] #<-- from column "col2" to column "col5"

# all rows and ... #df.iloc[:, 1] #<-- 1 column #df.iloc[:, [0,1]] #<-- more than one column df.iloc[:, 0:3] #<-- from column 0 to column 2

col1int64

1 - 10

col2int64

11 - 20